Gene Diversity Research Articles

First Detection of High-Level Aminoglycoside-Resistant Klebsiella pneumoniae and Enterobacter cloacae Isolates Due to 16S rRNA Methyltransferases with and Without blaNDM in Uruguay

Background: The increase in antimicrobial resistance includes emerging mechanisms such as 16S ribosomal RNA methylases, which confer high-level resistance to aminoglycosides. In this regard, the most predominant genes observed worldwide are rmtB and armA, but their presence in Uruguay is unknown. Objectives: We describe the genomic characterization of isolates carrying rmtB and rmtC, together with blaNDM-5 and blaNDM-1, respectively, and rmtD in our country. Methology: Five isolates from patients admitted to three hospitals were studied. Identification and antibiotic susceptibility testing were performed using the Vitek2 System. Whole Genome Sequencing was conducted, and hybrid assembly was performed with Unicycler. In silico analysis using the Center for Genomic Epidemiology’s tools was undertaken to predict antibiotic resistance determinants, plasmid incompatibility groups, and sequence types. Results: We report three K. pneumoniae ST307 isolates with an IncR plasmid carrying blaNDM-5/blaCTX-M-15/blaTEM-1B/rmtB/dfrA14/dfrA12/sul1/qacEΔ1/ermB/mphA, one K. pneumoniae ST258 harboring an IncC plasmid containing rmtC/blaNDM-1/blaCMY-6/aac(6′)-Ib/sul1, and one E. cloacae ST88 isolate with an IncFIB/II plasmid hosting rmtD, within a novel Tn21-like transposon named Tn7825, alongside blaOXA-101/sul1/tet(G)/floR, and a new variant of blaTEM assigned as blaTEM-258. One of the strains, named UH_B2, also carried an IncM1 plasmid encoding qnrE1/blaTEM-1/blaCTX-M-8 associated with ISEcp1. Conclusions: This is the first description of plasmids harboring 16S rRNA methyltransferases in Uruguay. The association and dissemination of diverse antibiotic-resistant genes underpin the health threat they represent, highlighting the lack of available antibiotics effective against multidrug-resistant microorganisms.

Conservation biology of three threatened Limonium species endemic to Zakynthos Island (Ionian Islands, Greece)

Abstract Knowledge of the life history traits, reproductive biology and demography of rare species is fundamental for their conservation, yet plant population monitoring is uncommon. The restricted ranges of the Limonium species endemic to the Mediterranean area, combined with the vulnerability of their specialized littoral habitats, indicate the need for appropriate conservation measures. We evaluate the conservation status and estimate the future extinction risk of three Limonium species endemic to Zakynthos Island in the Ionian Islands, Greece (Limonium korakonisicum, Limonium phitosianum and Limonium zacynthium) using 5 years of monitoring data. We compile information on their geographical distribution, population dynamics, reproductive biology and genetic diversity. Population sizes and survival rates of seedlings exhibited marked annual fluctuations, although fecundity and relative reproductive success remained high throughout the monitoring period. We observed a dominance of mature individuals in all three species, indicating their increased tolerance to salinity. Three subpopulations each of L. phitosianum and L. zacynthium were genotyped using five microsatellite loci. The observed number of alleles and the low gene flow value potentially indicate reduced genetic diversity, inbreeding, and limited gene flow within and among subpopulations of both species. Based on the IUCN categories and criteria, we assess L. korakonisicum as Critically Endangered, L. phitosianum as Near Threatened and L. zacynthium as Endangered. Population viability analyses predict that, among the three species, L. zacynthium will face the highest risk of extinction within the next 50 years. Knowledge of the biology of these species provides data essential for identifying critical factors for their survival and for proposing targeted conservation measures.

Antibiotic resistance and the search for resistance and virulence genes in Escherichia coli strains isolated from agropastoral farms (Poultry, Cattle, and Fish) In Southern Benin

Resistance of animal-origin enterobacteria to antibiotics is experiencing a concerning global increase, with an escalating impact of extended-spectrum beta-lactamases (ESBL). Finding solutions to this problem requires reliable and diverse data. It is in this context that the present study was initiated to combat infections caused by Escherichia coli in agropastoral farms (poultry, cattle, and fish) in southern Benin. Samples consisting of cattle dung, poultry droppings, as well as water and sludge from fish farms were aseptically collected for various laboratory analyses. A total of 714 E. coli strains were isolated from 1,376 collected samples, with 434 strains from the Atlantique department and 280 strains from the Zou department. The isolation of strains was carried out according to the NF V08-050: 2009 standard. The identified strains were tested against various antibiotics using the Bauer and Kirby disk diffusion method on agar, as recommended by the WHO. Strains resistant to one or more antibiotics were selected for the search for resistance and virulence genes. Furthermore, multidrug resistance of the strains to antibiotics was also observed, particularly in the Beta-lactam class (100% resistance to AMC) and tetracyclines (90% resistance to tetracyclines). Additionally, genotypic detection revealed a total absence of virulence genes, whereas resistance genes such as beta-lactamase Sulfhydryl Variable (bla-SHV), beta-lactamase Temoniera (bla-TEM), and quinolone resistance gene B (qnrB) were more frequently detected at respectively 58%, 82% and 60%.

The Effects of Traditional Asian Diet on Metabolism, Gut Microbiota, and Liver Tissue in NASH Rats

Traditional Asian Diets (AD) in rural areas have a significant risk of mortality due to cirrhosis and hepatocellular carcinoma as nonalcoholic fatty liver disease (NAFLD). This study aims to determine the relationship between AD and liver cancer cases using rat experimental animals Rattus norvegicus strain Wistar. The measured variables include metabolic parameters, gut microbiota profile, and liver histology. This study used 14 rats in two groups: Chow Diet (7 rats to CD) and AD (7 rats to AD), and were given the respective diets for 12 weeks. Enzyme-linked immunosorbent assay (ELISA) methods are used to analyze liver enzymes, lipid profiles, and blood sugar levels. The analysis of gut microbiota used variable region-specific 16S rRNA gene and V3-V4. Biopsy stained with Hematoxylin Eosin was used to study the histology of the liver. Moreover, it was analyzed utilizing NAS (NAFLD Activity Score). The result of this study indicated that reduce body weight the rats treated with AD significant different than treated with CD. Firmicutes, Lactobacillus reuteri, Prevotellaceae bacterium, Romboutsia ilealis, and Bacteroidota in AD greater than CD. Alzheimer's disease had notably higher levels of alkaline phosphatase compared to those diagnosed with Crohn's disease on individual diagnosis. Differences in total bilirubin, alanine transaminase, aspartate transaminase, blood sugar, total cholesterol, high-density lipoprotein, low-density lipoprotein, and triglycerides were not significant. The NAS analysis indicated that the two groups comprised rats lacking non-alcoholic fatty liver disease. Despite the high caloric content of the Asian diet, it did not lead to significant changes in metabolic parameters and liver histology related to non-alcoholic steatohepatitis. This behavior can be ascribed to the advantageous influence of the gut microbiota.

Detecting significant expression patterns in single-cell and spatial transcriptomics with a flexible computational approach

Gene expression data holds the potential to shed light on multiple biological processes at once. However, data analysis methods for single cell sequencing mostly focus on finding cell clusters or the principal progression line of the data. Data analysis for spatial transcriptomics mostly addresses clustering and finding spatially variable genes. Existing data analysis methods are effective in finding the main data features, but they might miss less pronounced, albeit significant, processes, possibly involving a subset of the samples. In this work we present SPIRAL: Significant Process InfeRence ALgorithm. SPIRAL is based on Gaussian statistics to detect all statistically significant biological processes in single cell, bulk and spatial transcriptomics data. The algorithm outputs a list of structures, each defined by a set of genes working simultaneously in a specific population of cells. SPIRAL is unique in its flexibility: the structures are constructed by selecting subsets of genes and cells based on statistically significant and consistent differential expression. Every gene and every cell may be part of one structure, more or none. SPIRAL also provides several visual representations of structures and pathway enrichment information. We validated the statistical soundness of SPIRAL on synthetic datasets and applied it to single cell, spatial and bulk RNA-sequencing datasets. SPIRAL is available at https://spiral.technion.ac.il/.

Diversity of the Chicken Growth Hormone Gene and Effects on Growth Desi and Fayoumi Chicken Traits at District Kashmore, Sindh-Pakistan

Chicken growth hormone (cGH) gene, one of the candidate genes for economic traits that control body weight and fat deposition, is associated with regulating both growth-axon patterning and metabolism. The growth hormone gene located on the chromosome 2 it enhances and encodes growth hormones as well as protein which are responsible for regulating of growth and development of body tissues. Growth hormone gene as economically very important because of growth rate, efficiency of feeds, body weight, and also for egg production. It also used for the Genetic Engineering for production of desirable GH- related genes. Genomic DNA from four different chicken breeds were screened for single nucleotide polymorphisms (SNPs) of cGH gene by denaturing high-performance liquid chromatography, as well as sequencing. SNPs and Nucleotide Diversity of the cGH Gene PCR amplification of 700 bp region from each individual from two chicken breeds was performed, which covered partial exons/introns encoding fragments corresponding to the investigated translated peptide. An average of one SNP was found every 86 bp, with a total of eight SNPs discovered. Nine SNPs were in introns, while 4 and one each was in the 5'UTR and 3'UTR respectively of these, five of them (01 from GWAS linkage signals) had significant associations.

Spatial pattern and differential expression analysis with spatial transcriptomic data.

The emergence of spatial transcriptomic technologies has opened new avenues for investigating gene activities while preserving the spatial context of tissues. Utilizing data generated by such technologies, the identification of spatially variable (SV) genes is an essential step in exploring tissue landscapes and biological processes. Particularly in typical experimental designs, such as case-control or longitudinal studies, identifying SV genes between groups is crucial for discovering significant biomarkers or developing targeted therapies for diseases. However, current methods available for analyzing spatial transcriptomic data are still in their infancy, and none of the existing methods are capable of identifying SV genes between groups. To overcome this challenge, we developed SPADE for spatial pattern and differential expression analysis to identify SV genes in spatial transcriptomic data. SPADE is based on a machine learning model of Gaussian process regression with a gene-specific Gaussian kernel, enabling the detection of SV genes both within and between groups. Through benchmarking against existing methods in extensive simulations and real data analyses, we demonstrated the preferred performance of SPADE in detecting SV genes within and between groups. The SPADE source code and documentation are publicly available at https://github.com/thecailab/SPADE.

The Type 1 Diabetes T Cell Receptor and B Cell Receptor Repository in the AIRR Data Commons: a practical guide for access, use and contributions through the Type 1 Diabetes AIRR Consortium.

Human molecular genetics has brought incredible insights into the variants that confer risk for the development of tissue-specific autoimmune diseases, including type 1 diabetes. The hallmark cell-mediated immune destruction that is characteristic of type 1 diabetes is closely linked with risk conferred by the HLA class II gene locus, in combination with a broad array of additional candidate genes influencing islet-resident beta cells within the pancreas, as well as function, phenotype and trafficking of immune cells to tissues. In addition to the well-studied germline SNP variants, there are critical contributions conferred by T cell receptor (TCR) and B cell receptor (BCR) genes that undergo somatic recombination to yield the Adaptive Immune Receptor Repertoire (AIRR) responsible for autoimmunity in type 1 diabetes. We therefore created the T1D TCR/BCR Repository (The Type 1 Diabetes T Cell Receptor and B Cell Receptor Repository) to study these highly variable and dynamic gene rearrangements. In addition to processed TCR and BCR sequences, the T1D TCR/BCR Repository includes detailed metadata (e.g. participant demographics, disease-associated parameters and tissue type). We introduce the Type 1 Diabetes AIRR Consortium goals and outline methods to use and deposit data to this comprehensive repository. Our ultimate goal is to facilitate research community access to rich, carefully annotated immune AIRR datasets to enable new scientific inquiry and insight into the natural history and pathogenesis of type 1 diabetes.

Scifer: An R/Bioconductor package for large-scale integration of Sanger sequencing and flow cytometry data of index-sorted single cells

Sanger sequencing remains widely used in various experimental contexts, often in combination with flow cytometry for indexing specific cell populations. However, existing software lacks the capability to automate quality control (QC) of raw Sanger sequencing data and integrate it with flow cytometry information on a large scale. Here, we introduce scifer, an R package now available in the latest release of Bioconductor (3.20) showcasing its effectiveness in seamlessly integrating these types of data as demonstrated by analyses of B cell and T cell receptor sequences. Scifer preprocesses raw data from index sorts and immune receptor Sanger sequencing. It identifies high-quality sequences based on selected parameters, such as length, Phred scores, and heavy-chain complementarity-determining region 3 (HCDR3) quality. As a result, the quality of germline assignments is significantly increased and spurious variable gene mutations are reduced. Scifer is automated and can process thousands of sequences in less than an hour. Its output provides quality control reports, FASTA files, summarized tables, and electropherograms for manual inspection. In summary, scifer is a user-friendly software that speeds up the analysis of immune receptor repertoire sequences, offering wide applicability.

Genome-Wide Identification and Characterization Thaumatin-like Protein (TLP) Genes in Wild Olive (Olea europaea var. sylvestris)

The wild olive tree (Olea europaea var. sylvestris) is the ancestor of all olive varieties, displaying remarkable adaptability to diverse climatic conditions and soil types. This iconic tree of the Mediterranean Basin stands out for its ability to withstand environmental stresses, such as water scarcity and temperature variations, playing a crucial role in the ecology, economy, and culture of the region. In this study, 42 TLP genes were characterized in the wild olive tree, distributed across 14 chromosomes and nine scaffolds. In silico analysis revealed the presence of domains associated with antifungal activity in some sequences, indicating their potential role in pathogen resistance. The structural diversity of OeTLP genes was explored through multiple alignments, conserved motifs and phenetic analysis. Correlation was observed between phenetic diversity, genetic structure, and motif patterns, suggesting different functions among OeTLP groups. Additionally, the distribution of exons and introns in the pheneticc tree provided further insights into the evolution of these genes. Comparative modeling of OeTLPs unveiled three-dimensional models with good structural quality and distinctive surface charge characteristics. This study provides a comprehensive understanding of the physicochemical, structural, and phenetic features of TLP genes in Wild Olive, contributing to knowledge about this species' response to environmental stresses and pathogens.

Gamma irradiation green synthesis of (polyacrylamide/chitosan/silver nanoparticles) hydrogel nanocomposites and their using as antifungal against Candida albicans and anti-cancer modulator

Silver nanoparticles-loaded hydrogel nanocomposites are exploited for medicinal and pharmaceutical applications. Hydrogel nanocomposites were prepared from acrylamide (Am), chitosan (CS) and AgNO3 utilizing gamma rays. Diverse variables were applied in preparation of silver nanoparticles-laoded hydrogel nanocomposites of (PAm/CS)-AgNPs such as influence of radiation dose and influnece of CS concentration. Diverse techniques were utilized to characterize hydrogel nanocomposites; Fourier transform infrared spectroscopy (FTIR), thermal gravimetric analysis (TGA), X-ray diffraction (XRD), Transmission electron microscopy (TEM), energy dispersive X-ray (EDX) and scanning electron microscopy (SEM). Results confirmed formation of silver nanoparticles-loaded hydrogel nanocomposites of (PAm/CS)-AgNPs. Antifungal activity of (PAm/CS)-AgNPs hydrogel nanocomposites on viability of C. albicans was esitmated. Results displayed the efficient microbial inhibition activity of treatment against C. albicans compared to control. Furthermore, (PAm/CS)-AgNPs hydrogel nanocomposite against cervical cancer HeLa cell line was investigated. Cytotoxicity of (PAm/CS)-AgNPs hydrogel nanocomposites on prior cancer cell line empolyed to prohibition of cell growth assesssed by MTT test. HeLa cancer cell is treated by (PAm/CS)-AgNPs for 48 h exposed a potential apoptotic activity by noticeable up-regulation of p53 gene expression. Moreover, anticancer activity was investigated by down-regulation of platelet-based growth variable receptor beta (PDGFR-β), Bcl2, Cathepsine, and MMP-2 gene expression. antioxidant activity was investigated and results showed antioxidant activity of (PAm/CS) hydrogel and (PAm/CS)-AgNPs hydrogel nanocomposite are 87.8% and 62.9%, respectively.

Autosomal recessive VWA1-related disorder: comprehensive analysis of phenotypic variability and genetic mutations.

A newly identified subtype of hereditary axonal motor neuropathy, characterized by early proximal limb involvement, has been discovered in a cohort of 34 individuals with biallelic variants in von Willebrand factor A domain-containing 1 (VWA1). This study further delineates the disease characteristics in a cohort of 20 individuals diagnosed through genome or exome sequencing, incorporating neurophysiological, laboratory and imaging data, along with data from previously reported cases across three different studies. Newly reported clinical features include hypermobility/hyperlaxity, axial weakness, dysmorphic signs, asymmetric presentation, dystonic features and, notably, upper motor neuron signs. Foot drop, foot deformities and distal leg weakness followed by early proximal leg weakness are confirmed to be initial manifestations. Additionally, this study identified 11 novel VWA1 variants, reaffirming the 10 bp insertion-induced p.Gly25ArgfsTer74 as the most prevalent disease-causing allele, with a carrier frequency of ∼1 in 441 in the UK and Western European population. Importantly, VWA1-related pathology may mimic various neuromuscular conditions, advocating for its inclusion in diverse gene panels spanning hereditary neuropathies to muscular dystrophies. The study highlights the potential of lower quality control filters in exome analysis to enhance diagnostic yield of VWA1 disease that may account for up to 1% of unexplained hereditary neuropathies.

Lactuca super-pangenome reduces bias towards reference genes in lettuce research

BackgroundBreeding of lettuce (Lactuca sativa L.), the most important leafy vegetable worldwide, for enhanced disease resistance and resilience relies on multiple wild relatives to provide the necessary genetic diversity. In this study, we constructed a super-pangenome based on four Lactuca species (representing the primary, secondary and tertiary gene pools) and comprising 474 accessions. We include 68 newly sequenced accessions to improve cultivar coverage and add important foundational breeding lines.ResultsWith the super-pangenome we find substantial presence/absence variation (PAV) and copy-number variation (CNV). Functional enrichment analyses of core and variable genes show that transcriptional regulators are conserved whereas disease resistance genes are variable. PAV-genome-wide association studies (GWAS) and CNV-GWAS are largely congruent with single-nucleotide polymorphism (SNP)-GWAS. Importantly, they also identify several major novel quantitative trait loci (QTL) for resistance against Bremia lactucae in variable regions not present in the reference lettuce genome. The usability of the super-pangenome is demonstrated by identifying the likely origin of non-reference resistance loci from the wild relatives Lactuca serriola, Lactuca saligna and Lactuca virosa.ConclusionsThe super-pangenome offers a broader view on the gene repertoire of lettuce, revealing relevant loci that are not in the reference genome(s). The provided methodology and data provide a strong basis for research into PAVs, CNVs and other variation underlying important biological traits of lettuce and other crops.

Mcadet: A feature selection method for fine-resolution single-cell RNA-seq data based on multiple correspondence analysis and community detection.

Single-cell RNA sequencing (scRNA-seq) data analysis faces numerous challenges, including high sparsity, a high-dimensional feature space, and biological noise. These challenges hinder downstream analysis, necessitating the use of feature selection methods to identify informative genes, and reduce data dimensionality. However, existing methods for selecting highly variable genes (HVGs) exhibit limited overlap and inconsistent clustering performance across benchmark datasets. Moreover, these methods often struggle to accurately select HVGs from fine-resolution scRNA-seq datasets and minority cell types, which are more difficult to distinguish, raising concerns about the reliability of their results. To overcome these limitations, we propose a novel feature selection framework for scRNA-seq data called Mcadet. Mcadet integrates Multiple Correspondence Analysis (MCA), graph-based community detection, and a novel statistical testing approach. To assess the effectiveness of Mcadet, we conducted extensive evaluations using both simulated and real-world data, employing unbiased metrics for comparison. Our results demonstrate the superior performance of Mcadet in the selection of HVGs in scenarios involving fine-resolution scRNA-seq datasets and datasets containing minority cell populations. Overall, we demonstrate that Mcadet enhances the reliability of selected HVGs, although the impact of HVG selection on various downstream analyses varies and needs to be further investigated.

Characterizing local pig breeds as reservoirs for the domestic pig genetic variability worldwide via contributions to gene diversity and allelic richness.

Contributions to gene diversity and allelic richness were computed in a total of 2,260 domestic pig genotypes, sharing 53,626 autosomal SNPs, belonging to 98 pig subpopulations worldwide (41 Asian, 696 genotypes; 20 American, 262 genotypes; and 37 European, 686 genotypes), using 616 pig samples belonging to six different Cosmopolitan pig breeds as an outgroup, to ascertain if local pig subpopulation can be considered reservoirs of genetic diversity for the whole domestic pig species worldwide. Assessments were carried out for the whole dataset and separately for the American-European and Asian subsets. Effective population size was computed at the subpopulation level using molecular coancestry and linkage disequilibrium information to ensure that estimates of contributions to diversity were not affected by demographic issues. Most American and European pig subpopulations tended to have favorable contributions to both gene diversity and allelic richness. However, contributions to allelic richness were more consistent than those obtained for gene diversity, whether the computations are performed using either the whole dataset or the American-Asian subset, suggesting that allelic richness can be a key parameter to identify putative reservoirs for the species. The Asian pig subpopulations never contributed favorably to the allelic richness of the domestic pig metapopulation. Although these results can partially be explained by the highly divergent origins of the American-European and the Asian pig subpopulations, it cannot be discarded that the results obtained for the Asian subpopulations are biased due to a worse calling performance of the pig SNP arrays used for genotyping. The use of other potentially less biased sources of genotypic information is advisable to compare the Asian and American-European pig subpopulations genetic diversity.

Adding Highly Variable Genes to Spatially Variable Genes Can Improve Cell Type Clustering Performance in Spatial Transcriptomics Data.

Spatial transcriptomics has allowed researchers to analyze transcriptome data in its tissue sample's spatial context. Various methods have been developed for detecting spatially variable genes (SV genes), whose gene expression over the tissue space shows strong spatial autocorrelation. Such genes are often used to define clusters in cells or spots downstream. However, highly variable (HV) genes, whose quantitative gene expressions show significant variation from cell to cell, are conventionally used in clustering analyses. In this report, we investigate whether adding highly variable genes to spatially variable genes can improve the cell type clustering performance in spatial transcriptomics data. We tested the clustering performance of HV genes, SV genes, and the union of both gene sets (concatenation) on over 50 real spatial transcriptomics datasets across multiple platforms, using a variety of spatial and non-spatial metrics. Our results show that combining HV genes and SV genes can improve overall cell-type clustering performance.

The impact of pine wilt disease on the endophytic microbial communities structure of Pinus koraiensis

Pine Wilt Disease (PWD) is a devastating pine tree disease characterized by rapid onset, high mortality rate, quick spread, and difficulty in control. Plant microbiome plays a significant role in the development of PWD. However, the endophytic microbial communities of Pinus koraiensis infected by pine wood nematode (PWN) Bursaphelenchus xylophilus remain largely unexplored. In this study, we analyzed the structural changes of endophytic communities of P. koraiensis after infection by the PWN using high-throughput sequencing technology. The results showed that the community structure underwent significant changes as the degree of PWN infection intensified. The diversity and abundance of endophytic fungi in P. koraiensis increased, while those of endophytic bacteria in P. koraiensis decreased during the infection process. Meanwhile, the abundance of some dominant microorganisms has also changed, including species such as Graphilbum and Pseudoalteromonas. Functional prediction analysis showed that the functional composition of endophytic fungi in P. koraiensis was significantly different across the development of PWD, while the composition of endophytic bacteria remained essentially similar. The results indicated that PWN infection had a significant impact on the structure, diversity, abundance, and functional gene composition of endophytic microbial communities in P. koraiensis, and most of the main endophytic microbial groups tended to coordinate with each other. This work provides a better understanding of the changes in endophytic community structure and function caused by PWD infection of P. koraiensis, which may benefit the exploration of potential endophytes for PWN biocontrol.

Single-cell Sequencing of Circulating Human Plasmablasts during Staphylococcus aureus Bacteremia.

Staphylococcus aureus is the major cause of healthcare-associated infections, including life-threatening conditions as bacteremia, endocarditis, and implant-associated infections. Despite adequate antibiotic treatment, the mortality of S. aureus bacteremia remains high. This calls for different strategies to treat this infection. In past years, sequencing of Ab repertoires from individuals previously exposed to a pathogen emerged as a successful method to discover novel therapeutic monoclonal Abs and understand circulating B cell diversity during infection. In this paper, we collected peripheral blood from 17 S. aureus bacteremia patients to study circulating plasmablast responses. Using single-cell transcriptome gene expression combined with sequencing of variable heavy and light Ig genes, we retrieved sequences from >400 plasmablasts revealing a high diversity with >300 unique variable heavy and light sequences. More than 200 variable sequences were synthesized to produce recombinant IgGs that were analyzed for binding to S. aureus whole bacterial cells. This revealed four novel monoclonal Abs that could specifically bind to the surface of S. aureus in the absence of Ig-binding surface SpA. Interestingly, three of four mAbs showed cross-reactivity with Staphylococcus epidermidis. Target identification revealed that the S. aureus-specific mAb BC153 targets wall teichoic acid, whereas cross-reactive mAbs BC019, BC020, and BC021 target lipoteichoic acid. All mAbs could induce Fc-dependent phagocytosis of staphylococci by human neutrophils. Altogether, we characterize the active B cell responses to S. aureus in infected patients and identify four functional mAbs against the S. aureus surface, of which three cross-react with S. epidermidis.

Genetic diversity and fingerprinting of Elaeagnus angustifolia based on SSR molecular markers

DNA fingerprinting can reveal the genetic diversity of Elaeagnus angustifolia germplasm resources and clarify the source and genetic background of E. angustifolia germplasm, which are the preconditions for the breeding of new varieties, the identification and protection of germplasm resources, and the comprehensive development of the E. angustifolia industry considering both ecological and economic benefits. We employed 11 pairs of primers with high polymorphism, clear bands, and high reproducibility to analyze the genetic diversity of 150 E. angustifolia germplasm accessions from Gansu and Beijing by the simple sequence repeat (SSR) molecular markers. We then employed the unweighted pair-group method with arithmetic means (UPGMA) to perform the cluster analysis based on genetic distance and analyzed the genetic structure of the 150 germplasm accessions based on a Bayesian model in Structure v2.3.3. The genetic diversity analysis revealed the mean number of alleles (Na) of 7.636 4, the mean number of effective alleles (Ne) of 2.832 6, the mean Shannon genetic diversity index (I) of 1.178 1, the mean Nei's gene diversity index (H) of 0.582 1, the mean observed heterozygosity (Ho) of 0.489 9, the mean expected heterozygosity (He) of 0.584 0, the mean polymorphism information content (PIC) of 0.535 4, and the mean genetic similarity (GS) of 0.831 5. These results suggested that the E. angustifolia germplasm resources we studied exhibited significant genetic differences and rich genetic diversity. The cluster analysis revealed that the tested materials can be classified into 3 groups, with the main genetic distance (GD) of 0.422 9. The clustering results were not completely consistent with the geographic origin. The population structure analysis classified the germplasm accessions into 2 populations. We used 8 pairs of primers with high PIC to construct the fingerprints of 150 E. angustifolia germplasm accessions. The present study successfully constructs the DNA fingerprints and clarified the genetic relationship of the E. angustifolia germplasm resources in Gansu and Beijing, providing a theoretical basis for germplasm resource identification, breeding of elite varieties, application in gardening, and molecular-assisted breeding of E. angustifolia.

Conserved sites on the influenza H1 and H3 hemagglutinin recognized by human antibodies.

Monoclonal antibodies (mAbs) targeting the influenza hemagglutinin (HA) have the potential to be used as prophylactics or templates for next-generation vaccines that provide broad protection. Here, we isolated broad, subtype-neutralizing mAbs from human B cells targeting the H1 or H3 HA head as well as a unique mAb targeting the stem. The H1 mAbs target the previously defined lateral patch epitope on H1 HAs and recognize HAs from 1933 to 2021 in addition to a swine H1N1 virus with pandemic potential. Using directed evolution, we improved the neutralization potency of these H1 mAbs towards a contemporary H1 strain. Using deep mutational scanning of four antigenically distinct H1N1 viruses, we identified potential viral escape pathways. For the H3 mAbs we used cryo-EM to define the targeted epitopes: one mAb recognizes the side of the H3 head, accommodating the N133 glycan and a pocket underneath the receptor binding site. The other H3 mAb recognizes an epitope in the HA stem that overlaps with previously characterized mAbs, but with distinct antibody variable genes and mode of recognition. Collectively, these mAbs identify common sites recognized by broad, subtype-specific mAbs that may be elicited by next-generation vaccines.