Current development of molecular classifications of gastric cancer based on omics (Review).

Gastric cancer (GC) is a complex and heterogeneous disease with significant phenotypic and genetic variation. Traditional classification systems rely mainly on the evaluation of clinical pathological features and conventional biomarkers and might not capture the diverse clinical processes of individual GCs. The latest discoveries in omics technologies such as next­generation sequencing, proteomics and metabolomics have provided crucial insights into potential genetic alterations and biological events in GC. Clustering strategies for identifying subtypes of GC might offer new tools for improving GC treatment and clinical trial outcomes by enabling the development of therapies tailored to specific subtypes. However, the feasibility and therapeutic significance of implementing molecular classifications of GC in clinical practice need to addressed. The present review examines the current molecular classifications, delineates the prevailing landscape of clinically relevant molecular features, analyzes their correlations with traditional GC classifications, and discusses potential clinical applications.

The Comparison of Newly Diagnosed Invasive Breast Patient Cohorts in Genomics Evidence Neoplasia Information Exchange Biopharma Collaborative (GENIE-BPC) and Other Real-World Databases.

BACKGROUND: Oncology databases that integrate genomic and clinical data have become valuable resources for precision medicine. However, the generalizability of these databases has not been comprehensively assessed. OBJECTIVES: To describe the demographics, clinical characteristics, treatments, and overall survival of breast cancer cohorts in GENIE-BPC and three other databases. METHODS: This study utilized GENIE-BPC, SEER, SEER-Medicare, and Merative MarketScan Research Databases. Women with invasive breast cancer were identified through EHR, cancer registries or ICD-9/10-CM codes. The ages were 18+ years or per database requirement. Treatments were based on EHR or HCPCS/NDC codes in claims. Overall survival was estimated as time from diagnosis to death. RESULTS: Of female breast cancer patients in GENIE-BPC (n = 775), SEER (n = 548 336), SEER-Medicare (n = 68 914), and Marketscan (n = 109 499) databases, the median ages at initial diagnosis were 44, 62, 74, and 57 years, respectively. A greater proportion of patients in GENIE-BPC, compared to SEER/SEER-Medicare, had higher nuclear grades (%III-%IV: 57% vs. 26%/24%), advanced disease stage (%IV: 25.3% vs. 5%/3.6%), percent of triple negative breast cancer (19.7% vs. 10.2%/8.5%), and receipt of chemotherapy (85.0% vs. NA/22.3%). The 1-, 3-, and 5-year overall survival rates were lower in GENIE-BPC (78.5%, 60.5%, 55.5%) than in SEER (95.8%, 89.5%, 85.5%) and SEER-Medicare (91.6%, 81.4%, 75.0%). CONCLUSION: Breast cancer patients in GENIE-BPC were younger, had more advanced disease, had a higher proportion of triple negative breast cancer and recipients of chemotherapy, and had poorer overall survival. Researchers must use statistical adjustment when extrapolating results (e.g., biomarker prevalence) from GENIE-BPC to the larger breast cancer population.

Molecular Evolutionary Dynamics of Coxsackievirus A6 Causing Hand, Foot, and Mouth Disease From 2021 to 2023 in China: Genomic Epidemiology Study.

Background: Hand, foot, and mouth disease (HFMD) is a global public health concern, notably within the Asia-Pacific region. Recently, the primary pathogen causing HFMD outbreaks across numerous countries, including China, is coxsackievirus (CV) A6, one of the most prevalent enteroviruses in the world. It is a new variant that has undergone genetic recombination and evolution, which might not only induce modifications in the clinical manifestations of HFMD but also heighten its pathogenicity because of nucleotide mutation accumulation. Objective: The study assessed the epidemiological characteristics of HFMD in China and characterized the molecular epidemiology of the major pathogen (CV-A6) causing HFMD. We attempted to establish the association between disease progression and viral genetic evolution through a molecular epidemiological study. Methods: Surveillance data from the Chinese Center for Disease Control and Prevention from 2021 to 2023 were used to analyze the epidemiological seasons and peaks of HFMD in Henan, China, and capture the results of HFMD pathogen typing. We analyzed the evolutionary characteristics of all full-length CV-A6 sequences in the NCBI database and the isolated sequences in Henan. To characterize the molecular evolution of CV-A6, time-scaled tree and historical population dynamics regarding CV-A6 sequences were estimated. Additionally, we analyzed the isolated strains for mutated or missing amino acid sites compared to the prototype CV-A6 strain. Results: The 2021-2023 epidemic seasons for HFMD in Henan usually lasted from June to August, with peaks around June and July. The monthly case reporting rate during the peak period ranged from 20.7% (4854/23,440) to 35% (12,135/34,706) of the total annual number of cases. Analysis of the pathogen composition of 2850 laboratory-confirmed cases identified 8 enterovirus serotypes, among which CV-A6 accounted for the highest proportion (652/2850, 22.88%). CV-A6 emerged as the major pathogen for HFMD in 2022 (203/732, 27.73%) and 2023 (262/708, 37.01%). We analyzed all CV-A6 full-length sequences in the NCBI database and the evolutionary features of viruses isolated in Henan. In China, the D3 subtype gradually appeared from 2011, and by 2019, all CV-A6 virus strains belonged to the D3 subtype. The VP1 sequences analyzed in Henan showed that its subtypes were consistent with the national subtypes. Furthermore, we analyzed the molecular evolutionary features of CV-A6 using Bayesian phylogeny and found that the most recent common ancestor of CV-A6 D3 dates back to 2006 in China, earlier than the 2011 HFMD outbreak. Moreover, the strains isolated in 2023 had mutations at several amino acid sites compared to the original strain. Conclusions: The CV-A6 virus may have been introduced and circulating covertly within China prior to the large-scale HFMD outbreak. Our laboratory testing data confirmed the fluctuation and periodic patterns of CV-A6 prevalence. Our study provides valuable insights into understanding the evolutionary dynamics of CV-A6.

CARE-ing for Indigenous nonhuman genomic data - rethinking our approach.

Earlier this year, an international group of scientists published a paper in ScienceAdvances on the draft genome of the little bush moa (Anomalopteryx didiformis), one of about nine species of extinct flightless birds endemic to Aotearoa (New Zealand). The genome was sequenced from the ancient DNA of a "poorly provenanced" fossil bone acquired by the Royal Ontario Museum. It held important clues about the moa's evolutionary history and aspects of its biology.

Tissue characterization at an enhanced resolution across spatial omics platforms with deep generative model.

Recent advances in spatial omics have expanded the spectrum of profiled molecular categories beyond transcriptomics. However, many of these technologies are constrained by limited spatial resolution, hindering our ability to deeply characterize intricate tissue architectures. Existing computational methods primarily focus on the resolution enhancement of transcriptomics data, lacking the adaptability to address the emerging spatial omics technologies that profile various omics types. Here, we introduce soScope, a unified generative framework designed to enhance data quality and spatial resolution for molecular profiles obtained from diverse spatial technologies. soScope aggregates multimodal tissue information from omics, spatial relations and images, and jointly infers omics profiles at enhanced resolutions with omics-specific modeling through distribution priors. With comprehensive evaluations on diverse spatial omics platforms, including Visium, Xenium, spatial-CUT&Tag, and slide-DNA/RNA-seq, soScope improves performances in identifying biologically meaningful intestine and kidney architectures, revealing embryonic heart structure that cannot be resolved at the original resolution and correcting sample and technical biases arising from sequencing and sample processing. Furthermore, soScope extends to spatial multiomics technology spatial-CITE-seq and spatial ATAC-RNA-seq, leveraging cross-omics reference for simultaneous multiomics enhancement. soScope provides a versatile tool to improve the utilization of continually expanding spatial omics technologies and resources.

Phenotype prediction using biologically interpretable neural networks on multi-cohort multi-omics data.

Integrating multi-omics data into predictive models has the potential to enhance accuracy, which is essential for precision medicine. In this study, we developed interpretable predictive models for multi-omics data by employing neural networks informed by prior biological knowledge, referred to as visible networks. These neural networks offer insights into the decision-making process and can unveil novel perspectives on the underlying biological mechanisms associated with traits and complex diseases. We tested the performance, interpretability and generalizability for inferring smoking status, subject age and LDL levels using genome-wide RNA expression and CpG methylation data from the blood of the BIOS consortium (four population cohorts, Ntotal = 2940). In a cohort-wise cross-validation setting, the consistency of the diagnostic performance and interpretation was assessed. Performance was consistently high for predicting smoking status with an overall mean AUC of 0.95 (95% CI: 0.90-1.00) and interpretation revealed the involvement of well-replicated genes such as AHRR, GPR15 and LRRN3. LDL-level predictions were only generalized in a single cohort with an R2 of 0.07 (95% CI: 0.05-0.08). Age was inferred with a mean error of 5.16 (95% CI: 3.97-6.35) years with the genes COL11A2, AFAP1, OTUD7A, PTPRN2, ADARB2 and CD34 consistently predictive. For both regression tasks, we found that using multi-omics networks improved performance, stability and generalizability compared to interpretable single omic networks. We believe that visible neural networks have great potential for multi-omics analysis; they combine multi-omic data elegantly, are interpretable, and generalize well to data from different cohorts.

Genetic diversity, demographic history, and selective signatures of Silkie chicken.

BACKGROUND: Silkie is a traditional Chinese chicken breed characterized by its unique combination of specialized morphological traits. While previous studies have focused on the genetic basis of these traits, the overall genomic characteristics of the Silkie breed remain largely unexplored. In this study, we employed whole genome resequencing data to examine the genetic diversity, selective signals and demographic history of the Silkie breed through comparative analyses with seven other Chinese indigenous breeds (IDGBs), a commercial breed, and the wild ancestor Red Jungle Fowl. RESULTS: In total, 20.8 million high-quality single nucleotide polymorphisms and 86 large structural variations were obtained. We discovered that Silkie exhibits a relatively high level of inbreeding and is genetically distinct from other IDGBs. Furthermore, our analysis indicated that Silkie has experienced a stronger historical population bottleneck and has a smaller effective population size compared with other IDGBs. We identified 45 putatively selected genes that are enriched in the melanogenesis pathway, which probably is related to the feather color. Among these genes, LMBR1 and PDSS2 have been previously associated with the extra toe and the hookless feathers, respectively. Six of the selected genes (KITLG, GSK3B, SOBP, CTBP1, ELMO2, SNRPN) are known to be associated with neurodevelopment and mental diseases in human, and are possibly related to the distinct behavior of Silkie. We further identified structural variants in Silkie and found previously reported variants linked to hyperpigmentation (END3), muff and beard (HOXB8), and Rose-comb phenotype (MNR2). Additionally, we found a 0.61 Mb inversion overlapping with the GMDS gene, which was previously linked to neurodevelopmental defects in zebrafish and humans. This may also be related to the behavior distinctiveness of Silkie. CONCLUSIONS: Our study revealed that Silkie is genetically distinct and relatively highly inbred compared to other IDGB chicken populations, possibly attributed to more prolong population bottlenecks and selective breeding practice. These results enhance our understanding of how domestication and selective breeding have shaped the genome of Silkie. These findings contribute to the broader field of domestication and avian genomics, and have implications for the future conservation and breeding efforts.

Current genomic deep learning models display decreased performance in cell type-specific accessible regions.

BACKGROUND: A number of deep learning models have been developed to predict epigenetic features such as chromatin accessibility from DNA sequence. Model evaluations commonly report performance genome-wide; however, cis regulatory elements (CREs), which play critical roles in gene regulation, make up only a small fraction of the genome. Furthermore, cell type-specific CREs contain a large proportion of complex disease heritability. RESULTS: We evaluate genomic deep learning models in chromatin accessibility regions with varying degrees of cell type specificity. We assess two modeling directions in the field: general purpose models trained across thousands of outputs (cell types and epigenetic marks) and models tailored to specific tissues and tasks. We find that the accuracy of genomic deep learning models, including two state-of-the-art general purpose models-Enformer and Sei-varies across the genome and is reduced in cell type-specific accessible regions. Using accessibility models trained on cell types from specific tissues, we find that increasing model capacity to learn cell type-specific regulatory syntax-through single-task learning or high capacity multi-task models-can improve performance in cell type-specific accessible regions. We also observe that improving reference sequence predictions does not consistently improve variant effect predictions, indicating that novel strategies are needed to improve performance on variants. CONCLUSIONS: Our results provide a new perspective on the performance of genomic deep learning models, showing that performance varies across the genome and is particularly reduced in cell type-specific accessible regions. We also identify strategies to maximize performance in cell type-specific accessible regions.

Genomic and induction evidence for bacteriophage contributions to sargassum-bacteria symbioses.

BACKGROUND: Symbioses between primary producers and bacteria are crucial for nutrient exchange that fosters host growth and niche adaptation. Yet, how viruses that infect bacteria (phages) influence these bacteria-eukaryote interactions is still largely unknown. Here, we investigate the role of viruses on the genomic diversity and functional adaptations of bacteria associated with pelagic sargassum. This brown alga has dramatically increased its distribution range in the Atlantic in the past decade and is predicted to continue expanding, imposing severe impacts on coastal ecosystems, economies, and human health. RESULTS: We reconstructed 73 bacterial and 3963 viral metagenome-assembled genomes (bMAGs and vMAGs, respectively) from coastal Sargassum natans VIII and surrounding seawater. S. natans VIII bMAGs were enriched in prophages compared to seawater (28% and 0.02%, respectively). Rhodobacterales and Synechococcus bMAGs, abundant members of the S. natans VIII microbiome, were shared between the algae and seawater but were associated with distinct phages in each environment. Genes related to biofilm formation and quorum sensing were enriched in S. natans VIII phages, indicating their potential to influence algal association in their bacterial hosts. In-vitro assays with a bacterial community harvested from sargassum surface biofilms and depleted of free viruses demonstrated that these bacteria are protected from lytic infection by seawater viruses but contain intact and inducible prophages. These bacteria form thicker biofilms when growing on sargassum-supplemented seawater compared to seawater controls, and phage induction using mitomycin C was associated with a significant decrease in biofilm formation. The induced metagenomes were enriched in genomic sequences classified as temperate viruses compared to uninduced controls. CONCLUSIONS: Our data shows that prophages contribute to the flexible genomes of S. natans VIII-associated bacteria. These prophages encode genes with symbiotic functions, and their induction decreases biofilm formation, an essential capacity for flexible symbioses between bacteria and the alga. These results indicate that prophage acquisition and induction contribute to genomic and functional diversification during sargassum-bacteria symbioses, with potential implications for algae growth. Video Abstract.

Super-enhancer omics in stem cell.

The hallmarks of stem cells, such as proliferation, self-renewal, development, differentiation, and regeneration, are critical to maintain stem cell identity which is sustained by genetic and epigenetic factors. Super-enhancers (SEs), which consist of clusters of active enhancers, play a central role in maintaining stemness hallmarks by specifically transcriptional model. The SE-navigated transcriptional complex, including SEs, non-coding RNAs, master transcriptional factors, Mediators and other co-activators, forms phase-separated condensates, which offers a toggle for directing diverse stem cell fate. With the burgeoning technologies of multiple-omics applied to examine different aspects of SE, we firstly raise the concept of "super-enhancer omics", inextricably linking to Pan-omics. In the review, we discuss the spatiotemporal organization and concepts of SEs, and describe links between SE-navigated transcriptional complex and stem cell features, such as stem cell identity, self-renewal, pluripotency, differentiation and development. We also elucidate the mechanism of stemness and oncogenic SEs modulating cancer stem cells via genomic and epigenetic alterations hijack in cancer stem cell. Additionally, we discuss the potential of targeting components of the SE complex using small molecule compounds, genome editing, and antisense oligonucleotides to treat SE-associated organ dysfunction and diseases, including cancer. This review also provides insights into the future of stem cell research through the paradigm of SEs.

Australian Attitudes Towards Waivers of Consent Within the Context of Genomic Data Sharing.

This research identifies the circumstances in which Human Research Ethics Committees (HRECs) are trusted by Australians to approve the use of genomic data - without express consent - and considers the impact of genomic data sharing settings, and respondent attributes, on public trust. Survey results (N = 3013) show some circumstances are more conducive to public trust than others, with waivers endorsed when future research is beneficial and when privacy is protected, but receiving less support in other instances. Still, results imply attitudes are influenced by more than these specific circumstances, with different data sharing settings, and participant attributes, affecting views. Ultimately, this research raises questions and concerns in relation to the criteria HRECs use when authorising waivers of consent in Australia.

A model-free and distribution-free multi-omics integration approach for detecting novel lung adenocarcinoma genes.

Detection of important genes affecting lung adenocarcinoma (LUAD) is critical to finding effective therapeutic targets for this highly lethal cancer. However, many existing approaches have focused on single outcomes or phenotypic associations, which may not be as thorough as investigating molecular transcript levels within cells. In this article, we apply a novel multivariate rank-distance correlation-based gene selection procedure (MrDcGene) to LUAD multi-omics data downloaded from The Cancer Genome Atlas (TCGA). MrDcGene provides additional opportunities for detecting novel susceptibility genes as it leverages information from multiple platforms, while efficiently handling challenges such as high dimensionality, low signal-to-noise ratio, unknown distributions, and non-linear structures, etc. Notably, the MrDcGene method is able to detect two different scenarios, i.e., strong association strength with a few gene expressions and weak association strength with several gene expressions. After thoroughly exploring the association between gene expression (GE) and multiple other platforms, including reverse phase protein array (RPPA), miRNA, copy number variation (CNV) and DNA methylation (ME), we detect several novel genes that may play an important role in LUAD (ZNF133, CCDC159, YWHAZ, HNRNPR. ITPR2, PTHLH, and WIPI2). In addition, we quantitatively validate several other susceptibility genes that were reported in the literature using different methods and studies. The accuracy of the MrDcGene approach is theoretically assured and empirically demonstrated by the simulation studies.

Beyond tobacco: genomic disparities in lung cancer between smokers and never-smokers.

BACKGROUND: Tobacco use is one of the main risk factors for Lung Cancer (LC) development. However, about 10-20% of those diagnosed with the disease are never-smokers. For Non-Small Cell Lung Cancer (NSCLC) there are clear differences in both the clinical presentation and the tumor genomic profiles between smokers and never-smokers. For example, the Lung Adenocarcinoma (LUAD) histological subtype in never-smokers is predominately found in young women of European, North American, and Asian descent. While the clinical presentation and tumor genomic profiles of smokers have been widely examined, never-smokers are usually underrepresented, especially those of a Latin American (LA) background. In this work, we characterize, for the first time, the difference in the genomic profiles between smokers and never-smokers LC patients from Chile. METHODS: We conduct a comparison by smoking status in the frequencies of genomic alterations (GAs) including somatic mutations and structural variants (fusions) in a total of 10 clinically relevant genes, including the eight most common actionable genes for LC (EGFR, KRAS, ALK, MET, BRAF, RET, ERBB2, and ROS1) and two established driver genes for malignancies other than LC (PIK3CA and MAP2K1). Study participants were grouped as either smokers (current and former, n = 473) or never-smokers (n = 200) according to self-report tobacco use at enrollment. RESULTS: Our findings indicate a higher overall GA frequency for never-smokers compared to smokers (58 vs. 45.7, p-value < 0.01) with the genes EGFR, KRAS, and PIK3CA displaying the highest prevalence while ERBB2, RET, and ROS1 the lowest. Never-smokers present higher frequencies in seven out of the 10 genes; however, smokers harbor a more complex genomic profile. The clearest differences between groups are seen for EGFR (15.6 vs. 21.5, p-value: < 0.01), PIK3CA (6.8 vs 9.5) and ALK (3.2 vs 7.5) in favor of never-smokers, and KRAS (16.3 vs. 11.5) and MAP2K1 (6.6 vs. 3.5) in favor of smokers. Alterations in these genes are comprised almost exclusively by somatic mutations in EGFR and mainly by fusions in ALK, and only by mutations in PIK3CA, KRAS and MAP2K1. CONCLUSIONS: We found clear differences in the genomic landscape by smoking status in LUAD patients from Chile, with potential implications for clinical management in these limited-resource settings.

Association of XRCC2 with breast cancer, a multi-omics analysis at genomic, transcriptomic, and epigenomic level.

One of the main causes of cancer-related mortality for women worldwide is breast cancer (BC). The XRCC2 gene, essential for DNA repair, has been implicated in cancer susceptibility. This study aims to evaluate the association between XRCC2 and BC risk. The study was conducted at Zheen International Hospital in Erbil, Iraq, between 2021 and 2024 with a total of 88 samples, including 44 paired normal and cancer tissue samples. Mutation analysis was performed using Next-Generation Sequencing, coupled with in silico tools for variant impact prediction. Expression levels were assessed through RT-PCR, and methylation status was determined using methylation-sensitive restriction enzyme digestion PCR. The study identified seven inherited germline variants in the XRCC2 gene, with five of these mutations being Uncertain Significance, one being Likely Pathogenic, and one being Likely benign. RNA purity was found high with mean A260/280 ratios of 1.986 ± 0.097 in normal (N) and 1.963 ± 0.092 in tumor (T) samples. Tumor samples exhibited a higher RNA concentration (78.56 ± 40.87 ng/µL) than normal samples (71.44 ± 40.79 ng/µL). XRCC2 gene expression was significantly upregulated in tumor tissue, with marked increases in patients aged 40-55 and >56 years and in higher cancer grades (II and III) and invasive ductal carcinoma (p-values ranging from <0.0001 to 0.0392). DNA methylation rates in tumor tissues were low (7%), suggesting limited regulation by methylation. The study suggests that XRCC2 can be classified as an oncogene and that its structural investigation by targeted NGS and expression evaluation can be used as a potential biomarker in BC.

Complete de novo assembly of Wolbachia endosymbiont of Drosophila willistoni using long-read genome sequencing.

Wolbachia is an obligate intracellular α-proteobacterium, which commonly infects arthropods and filarial nematodes. Different strains of Wolbachia are capable of a wide range of regulatory manipulations in their diverse hosts, including the modulation of host cellular differentiation to influence host reproduction. The genetic basis for the majority of these phenotypes is unknown. The wWil strain from the neotropical fruit fly, Drosophila willistoni, exhibits a remarkably high affinity for host germline-derived cells relative to the somatic cells. This trait could be leveraged for understanding how Wolbachia influences the host germline and for controlling host populations in the field. To further the use of this strain in biological and biomedical research, we sequenced the genome of the wWil strain isolated from host cell culture cells. Here, we present the first high quality Nanopore assembly of wWil, the Wolbachia endosymbiont of D. willistoni. Our assembly resulted in a circular genome of 1.27 Mb with a BUSCO completeness score of 99.7%. Consistent with other insect-associated Wolbachia strains, comparative genomic analysis revealed that wWil has a highly mosaic genome relative to the closely related wMel and wAu strains from Drosophila melanogaster and Drosophila simulans, respectively.

Ecological genomics in the Northern krill uncovers loci for local adaptation across ocean basins.

Krill are vital as food for many marine animals but also impacted by global warming. To learn how they and other zooplankton may adapt to a warmer world we studied local adaptation in the widespread Northern krill (Meganyctiphanes norvegica). We assemble and characterize its large genome and compare genome-scale variation among 74 specimens from the colder Atlantic Ocean and warmer Mediterranean Sea. The 19 Gb genome likely evolved through proliferation of retrotransposons, now targeted for inactivation by extensive DNA methylation, and contains many duplicated genes associated with molting and vision. Analysis of 760 million SNPs indicates extensive homogenizing gene-flow among populations. Nevertheless, we detect signatures of adaptive divergence across hundreds of genes, implicated in photoreception, circadian regulation, reproduction and thermal tolerance, indicating polygenic adaptation to light and temperature. The top gene candidate for ecological adaptation was nrf-6, a lipid transporter with a Mediterranean variant that may contribute to early spring reproduction. Such variation could become increasingly important for fitness in Atlantic stocks. Our study underscores the widespread but uneven distribution of adaptive variation, necessitating characterization of genetic variation among natural zooplankton populations to understand their adaptive potential, predict risks and support ocean conservation in the face of climate change.

MIPs: multi-locus intron polymorphisms in species identification and population genomics.

The study of species groups in which the presence of interspecific hybridization or introgression phenomena is known or suspected involves analysing shared bi-parentally inherited molecular markers. Current methods are based on different categories of markers among which the classical microsatellites or the more recent genome wide approaches for the analyses of thousands of SNPs or hundreds of microhaplotypes through high throughput sequencing. Our approach utilizes intron-targeted amplicon sequencing to characterise multi-locus intron polymorphisms (MIPs) and assess genetic diversity. These highly variable intron regions, combined with inter-specific transferable loci, serve as powerful multiple-SNP markers potentially suitable for various applications, from species and hybrid identification to population comparisons, without prior species knowledge. We developed the first panel of MIPs highly transferable across fish genomes, effectively distinguishing between species, even those closely related, and populations with different structures. MIPs offer versatile, hypervariable nuclear markers and promise to be especially useful when multiple nuclear loci must be genotyped across different species, such as for the monitoring of interspecific hybridization. Moreover, the relatively long sequences obtained ease the development of single-locus PCR-based diagnostic markers. This method, here demonstrated in teleost fishes, can be readily applied to other taxa, unlocking a new source of genetic variation.

Benchmarking and integrating human B-cell receptor genomic and antibody proteomic profiling.

Immunoglobulins (Ig), which exist either as B-cell receptors (BCR) on the surface of B cells or as antibodies when secreted, play a key role in the recognition and response to antigenic threats. The capability to jointly characterize the BCR and antibody repertoire is crucial for understanding human adaptive immunity. From peripheral blood, bulk BCR sequencing (bulkBCR-seq) currently provides the highest sampling depth, single-cell BCR sequencing (scBCR-seq) allows for paired chain characterization, and antibody peptide sequencing by tandem mass spectrometry (Ab-seq) provides information on the composition of secreted antibodies in the serum. Yet, it has not been benchmarked to what extent the datasets generated by these three technologies overlap and complement each other. To address this question, we isolated peripheral blood B cells from healthy human donors and sequenced BCRs at bulk and single-cell levels, in addition to utilizing publicly available sequencing data. Integrated analysis was performed on these datasets, resolved by replicates and across individuals. Simultaneously, serum antibodies were isolated, digested with multiple proteases, and analyzed with Ab-seq. Systems immunology analysis showed high concordance in repertoire features between bulk and scBCR-seq within individuals, especially when replicates were utilized. In addition, Ab-seq identified clonotype-specific peptides using both bulk and scBCR-seq library references, demonstrating the feasibility of combining scBCR-seq and Ab-seq for reconstructing paired-chain Ig sequences from the serum antibody repertoire. Collectively, our work serves as a proof-of-principle for combining bulk sequencing, single-cell sequencing, and mass spectrometry as complementary methods towards capturing humoral immunity in its entirety.

Beef Cattle Genome Project: Advances in Genome Sequencing, Assembly, and Functional Genes Discovery.

Beef is a major global source of protein, playing an essential role in the human diet. The worldwide production and consumption of beef continue to rise, reflecting a significant trend. However, despite the critical importance of beef cattle resources in agriculture, the diversity of cattle breeds faces severe challenges, with many breeds at risk of extinction. The initiation of the Beef Cattle Genome Project is crucial. By constructing a high-precision functional annotation map of their genome, it becomes possible to analyze the genetic mechanisms underlying important traits in beef cattle, laying a solid foundation for breeding more efficient and productive cattle breeds. This review details advances in genome sequencing and assembly technologies, iterative upgrades of the beef cattle reference genome, and its application in pan-genome research. Additionally, it summarizes relevant studies on the discovery of functional genes associated with key traits in beef cattle, such as growth, meat quality, reproduction, polled traits, disease resistance, and environmental adaptability. Finally, the review explores the potential of telomere-to-telomere (T2T) genome assembly, structural variations (SVs), and multi-omics techniques in future beef cattle genetic breeding. These advancements collectively offer promising avenues for enhancing beef cattle breeding and improving genetic traits.

Genomic study of European Clostridioides difficile ribotype 002/sequence type 8.

Clostridioides difficile has significant clinical importance as a leading cause of healthcare-associated infections, with symptoms ranging from mild diarrhoea to severe colitis, and possible life-threatening complications. C. difficile ribotype (RT) 002, mainly associated with MLST sequence type (ST) 8, is one of the most common RTs found in humans. This study aimed at investigating the genetic characteristics of 537 C. difficile genomes of ST8/RT002. To this end, we sequenced 298 C. difficile strains representing a new European genome collection, with strains from Germany, Denmark, France and Portugal. These sequences were analysed against a global dataset consisting of 1,437 ST8 genomes available through Enterobase. Our results showed close genetic relatedness among the studied ST8 genomes, a diverse array of antimicrobial resistance (AMR) genes and the presence of multiple mobile elements. Notably, the pangenome analysis revealed an open genomic structure. ST8 shows relatively low overall variation. Thus, clonal isolates were found across different One Health sectors (humans, animals, environment and food), time periods, and geographical locations, suggesting the lineage's stability and a universal environmental source. Importantly, this stability did not hinder the acquisition of AMR genes, emphasizing the adaptability of this bacterium to different selective pressures. Although only 2.4 % (41/1,735) of the studied genomes originated from non-human sources, such as animals, food, or the environment, we identified 9 cross-sectoral core genome multilocus sequence typing (cgMLST) clusters. Our study highlights the importance of ST8 as a prominent lineage of C. difficile with critical implications in the context of One Health. In addition, these findings strongly support the need for continued surveillance and investigation of non-human samples to gain a more comprehensive understanding of the epidemiology of C. difficile.