Omics approaches in asthma research: Challenges and opportunities.

Asthma, a chronic respiratory disease with a global prevalence of approximately 300 million individuals, presents a significant societal and economic burden. This multifaceted syndrome exhibits diverse clinical phenotypes and pathogenic endotypes influenced by various factors. The advent of omics technologies has revolutionized asthma research by delving into the molecular foundation of the disease to unravel its underlying mechanisms. Omics technologies are employed to systematically screen for potential biomarkers, encompassing genes, transcripts, methylation sites, proteins, and even the microbiome components. This review provides an insightful overview of omics applications in asthma research, with a special emphasis on genetics, transcriptomics, epigenomics, and the microbiome. We explore the cutting-edge methods, discoveries, challenges, and potential future directions in the realm of asthma omics research. By integrating multi-omics and non-omics data through advanced statistical techniques, we aspire to advance precision medicine in asthma, guiding diagnosis, risk assessment, and personalized treatment strategies for this heterogeneous condition.

Immune-related gene characteristics: A new chapter in precision treatment of gastric cancer.

Gastric cancer ranks as the sixth most prevalent cancer worldwide. In recent research within the realm of gastric cancer treatment, the identification and application of immune-related genetic features have emerged as groundbreaking advancements. The study by Ma et al, which developed a prognostic model based on 10 genes, categorizes patients into high and low-risk groups to predict their responsiveness to immune checkpoint inhibitor therapy. This research underscores the potential of immune-related genes as biomarkers for personalized treatment, offering insights into tumor mutation burden and immune phenotype scores. We advocate for further validation, understanding of biological mechanisms, and integration of diverse datasets to enhance the model's predictive accuracy and clinical application, marking a significant step towards personalized and precise treatment for gastric cancer.

Inference of host-pathogen interaction matrices from genome-wide polymorphism data.

Host-pathogen coevolution is defined as the reciprocal evolutionary changes in both species due to genotype x genotype (GxG) interactions at the genetic level determining the outcome and severity of infection. While co-analyses of host and pathogen genomes (co-GWAs) allow us to pinpoint the interacting genes, these do not reveal which host genotype(s) is/are resistant to which pathogen genotype(s). The knowledge of this so-called infection matrix is important for agriculture and medicine. Building on established theories of host-pathogen interactions, we here derive four novel indices capturing the characteristics of the infection matrix. These indices can be computed from full genome polymorphism data of randomly sampled uninfected hosts, as well as infected hosts and their pathogen strains. We use these indices in an Approximate Bayesian Computation method to pinpoint loci with relevant GxG interactions and to infer their underlying interaction matrix. In a combined SNP data set of 451 European humans and their infecting Hepatitis C Virus (HCV) strains and 503 uninfected individuals, we reveal a new human candidate gene for resistance to HCV and new virus mutations matching human genes. For two groups of significant human-HCV (GxG) associations, we infer a gene-for-gene infection matrix, which is commonly assumed to be typical of plant-pathogen interactions. Our model-based inference framework bridges theoretical models of GxG interactions with host and pathogen genomic data. It, therefore, paves the way for understanding the evolution of key GxG interactions underpinning HCV adaptation to the European human population after a recent expansion.

Genetic Signatures of Positive Selection in Human Populations Adapted to High Altitude in Papua New Guinea.

Papua New Guinea (PNG) hosts distinct environments mainly represented by the ecoregions of the Highlands and Lowlands that display increased altitude and a predominance of pathogens, respectively. Since its initial peopling approximately 50,000 years ago, inhabitants of these ecoregions might have differentially adapted to the environmental pressures exerted by each of them. However, the genetic basis of adaptation in populations from these areas remains understudied. Here, we investigated signals of positive selection in 62 highlanders and 43 lowlanders across 14 locations in the main island of PNG using whole-genome genotype data from the Oceanian Genome Variation Project (OGVP) and searched for signals of positive selection through population differentiation and haplotype-based selection scans. Additionally, we performed archaic ancestry estimation to detect selection signals in highlanders within introgressed regions of the genome. Among highland populations we identified candidate genes representing known biomarkers for mountain sickness (SAA4, SAA1, PRDX1, LDHA) as well as candidate genes of the Notch signaling pathway (PSEN1, NUMB, RBPJ, MAML3), a novel proposed pathway for high altitude adaptation in multiple organisms. We also identified candidate genes involved in oxidative stress, inflammation, and angiogenesis, processes inducible by hypoxia, as well as in components of the eye lens and the immune response. In contrast, candidate genes in the lowlands are mainly related to the immune response (HLA-DQB1, HLA-DQA2, TAAR6, TAAR9, TAAR8, RNASE4, RNASE6, ANG). Moreover, we find two candidate regions to be also enriched with archaic introgressed segments, suggesting that archaic admixture has played a role in the local adaptation of PNG populations.

A systematic review of genomic education for nurses and nursing students: Are they sufficient in the era of precision health?

BACKGROUND: Integration of genomic content into nursing curricula and continuing education is limited. PURPOSE: This systematic review aimed to identify educational strategies to deliver genomics content. METHODS: CINAHL Plus, ERIC, Ovid MEDLINE, and Scopus electronic databases were searched from January 1, 2003 through July 6, 2023. The three domains of learning: cognitive, affective, and psychomotor were used to categorize educational outcomes. A narrative approach was used to synthesize the data using Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. DISCUSSION: Ten studies met inclusion criteria. Quality assessment for bias was conducted independently; study quality ranged from fair to poor. No study used a complete psychometrically tested instrument to measure genomic cognitive or affective knowledge; none measured the psychomotor domain. CONCLUSIONS: Effective strategies to deliver genomic education are a priority. Development of reliable and valid instruments for the assessment of educational interventions, including the psychomotor skills needed for practice.

Enhancing metagenomic classification with compression-based features.

Metagenomics is a rapidly expanding field that uses next-generation sequencing technology to analyze the genetic makeup of environmental samples. However, accurately identifying the organisms in a metagenomic sample can be complex, and traditional reference-based methods may need to be more effective in some instances. In this study, we present a novel approach for metagenomic identification, using data compressors as a feature for taxonomic classification. By evaluating a comprehensive set of compressors, including both general-purpose and genomic-specific, we demonstrate the effectiveness of this method in accurately identifying organisms in metagenomic samples. The results indicate that using features from multiple compressors can help identify taxonomy. An overall accuracy of 95% was achieved using this method using an imbalanced dataset with classes with limited samples. The study also showed that the correlation between compression and classification is insignificant, highlighting the need for a multi-faceted approach to metagenomic identification. This approach offers a significant advancement in the field of metagenomics, providing a reference-less method for taxonomic identification that is both effective and efficient while revealing insights into the statistical and algorithmic nature of genomic data. The code to validate this study is publicly available at https://github.com/ieeta-pt/xgTaxonomy.

Integrated genomic/epigenomic analysis stratifies subtypes of clear cell ovarian carcinoma, highlighting their cellular origin.

The cellular origin of clear cell ovarian carcinoma (CCOC), a major histological subtype of ovarian carcinoma remains elusive. Here, we explored the candidate cellular origin and identify molecular subtypes using integrated genomic/epigenomic analysis. We performed whole exome-sequencing, microarray, and DNA methylation array in 78 CCOC samples according to the original diagnosis. The findings revealed that ARID1A and/or PIK3CA mutations were mutually exclusive with DNA repair related genes, including TP53, BRCA1, and ATM. Clustering of CCOC and other ovarian carcinomas (n = 270) with normal tissues from the fallopian tube, ovarian surface epithelium, endometrial epithelium, and pelvic peritoneum mesothelium (PPM) in a methylation array showed that major CCOC subtypes (with ARID1A and/or PIK3CA mutations) were associated with the PPM-lile cluster (n = 64). This cluster was sub-divided into three clusters: (1) mismatch repair (MMR) deficient with tumor mutational burden-high (n = 2), (2) alteration of ARID1A (n = 51), and (3) ARID1A wild-type (n = 11). The remaining samples (n = 14) were subdivided into (4) ovarian surface epithelium-like (n = 11) and (5) fallopian tube-like (considered as high-grade serous histotype; n = 3). Among these, subtypes (1-3) and others (4 and 5) were found to be associated with immunoreactive signatures and epithelial-mesenchymal transition, respectively. These results contribute to the stratification of CCOC into biological subtypes.

Ecological phage therapy: Can bacteriophages help rapidly restore the soil microbiome?

Soil microbiota underpin ecosystem functionality yet are rarely targeted during ecosystem restoration. Soil microbiota recovery following native plant revegetation can take years to decades, while the effectiveness of soil inoculation treatments on microbiomes remains poorly explored. Therefore, innovative restoration treatments that target soil microbiota represent an opportunity to accelerate restoration outcomes. Here, we introduce the concept of ecological phage therapy-the application of phage for the targeted reduction of the most abundant and dominant bacterial taxa present in degraded ecosystems. We propose that naturally occurring bacteriophages-viruses that infect bacteria-could help rapidly shift soil microbiota towards target communities. Bacteriophages sculpt the microbiome by lysis of specific bacteria, and if followed by the addition of reference soil microbiota, such treatments could facilitate rapid reshaping of soil microbiota. Here, we experimentally tested this concept in a pilot study. We collected five replicate pre-treatment degraded soil samples, then three replicate soil samples 48 hours after phage, bacteria, and control treatments. Bacterial 16S rDNA sequencing showed that phage-treated soils had reduced bacterial diversity; however, when we combined ecological phage therapy with reference soil inoculation, we did not see a shift in soil bacterial community composition from degraded soil towards a reference-like community. Our pilot study provides early evidence that ecological phage therapy could help accelerate the reshaping of soil microbiota with the ultimate aim of reducing timeframes for ecosystem recovery. We recommend the next steps for ecological phage therapy be (a) developing appropriate risk assessment and management frameworks, and (b) focussing research effort on its practical application to maximise its accessibility to restoration practitioners.

Protocol for preparing mammalian skin samples encompassing hair follicles for spatial transcriptomics.

Spatial transcriptomics enables a single-cell resolution view of gene expression patterns in tissues, providing insight into their biological functions. However, applying this approach to the skin presents inherent challenges. Here, we present a protocol for preparing mammalian skin samples encompassing hair follicles for spatial transcriptomics. We describe steps for sample preparation, embedding, acquisition of frozen slices, RNA quality control, tissue mounting, fixation, staining, and imaging. We then detail procedures for permeabilization, reverse transcription, and cDNA collection. For complete details on the use and execution of this protocol, please refer to Chen et al.1.

Evaluating large language models on medical, lay language, and self-reported descriptions of genetic conditions.

Large language models (LLMs) are generating interest in medical settings. For example, LLMs can respond coherently to medical queries by providing plausible differential diagnoses based on clinical notes. However, there are many questions to explore, such as evaluating differences between open- and closed-source LLMs as well as LLM performance on queries from both medical and non-medical users. In this study, we assessed multiple LLMs, including Llama-2-chat, Vicuna, Medllama2, Bard/Gemini, Claude, ChatGPT3.5, and ChatGPT-4, as well as non-LLM approaches (Google search and Phenomizer) regarding their ability to identify genetic conditions from textbook-like clinician questions and their corresponding layperson translations related to 63 genetic conditions. For open-source LLMs, larger models were more accurate than smaller LLMs: 7b, 13b, and larger than 33b parameter models obtained accuracy ranges from 21%-49%, 41%-51%, and 54%-68%, respectively. Closed-source LLMs outperformed open-source LLMs, with ChatGPT-4 performing best (89%-90%). Three of 11 LLMs and Google search had significant performance gaps between clinician and layperson prompts. We also evaluated how in-context prompting and keyword removal affected open-source LLM performance. Models were provided with 2 types of in-context prompts: list-type prompts, which improved LLM performance, and definition-type prompts, which did not. We further analyzed removal of rare terms from descriptions, which decreased accuracy for 5 of 7 evaluated LLMs. Finally, we observed much lower performance with real individuals' descriptions; LLMs answered these questions with a maximum 21% accuracy.

Single-center analysis of a real-world cohort of patients with metastatic urothelial carcinoma evaluated by NGS: molecular landscape and efficacy of targeted therapies.

PURPOSE: To describe the molecular profile of a real-world cohort of patients with metastatic urothelial carcinoma (mUC) and to evaluate the benefit of next-generation sequencing (NGS) panels in guiding therapy in patients with mUC and the outcomes of DNA-matched treatments recommended by a multidisciplinary molecular tumor board (MMTB). METHODS: This was a single-center analysis of a real-world cohort of adult patients with mUC included in an ongoing trial that aimed to evaluate the clinical utility of NGS for solid tumors. Genomic analysis was performed for each patient, most of them using the Ion Torrent Oncomine Focus Assay. Genomic results were discussed during MMTB meetings. RESULTS: We included 43 patients with mUC treated with platinum-based combinations and immunotherapy. Twenty-five patients (58.1%; 95% CI 43.4-72.9) had at least one tumor pathogenic alteration. The MMTB classified 16 (48.5%) of the 33 tumor pathogenic alterations found in our real-world cohort of mUC patients as ESCAT I, which is the maximum grade of actionability. After excluding patients who were not candidates for targeted therapies, the MMTB provided guidance on matched therapy for seven patients. Among these patients, three achieved a partial response for an overall response rate of 42.9%, a median progression-free survival of 7.3 months (95% CI 6.7-7.9) and a median overall survival of 10.9 months (95% CI 2.4-19.5). CONCLUSIONS: We recommend that all patients with mUC undergo NGS at diagnosis given the high percentage of patients with pathogenic alterations in our real-world cohort and the efficacy data of patients treated with targeted therapies.

DNA Methylation Patterns Associated with Tinnitus in Young Adults-A Pilot Study.

PURPOSE: Tinnitus, the perception of sound without any external sound source, is a prevalent hearing health concern. Mounting evidence suggests that a confluence of genetic, environmental, and lifestyle factors can influence the pathogenesis of tinnitus. We hypothesized that alteration in DNA methylation, an epigenetic modification that occurs at cytosines of cytosine-phosphate-guanine (CpG) dinucleotide sites, where a methyl group from S-adenyl methionine gets transferred to the fifth carbon of the cytosine, could contribute to tinnitus. DNA methylation patterns are tissue-specific, but the tissues involved in tinnitus are not easily accessible in humans. This pilot study used saliva as a surrogate tissue to identify differentially methylated CpG regions (DMRs) associated with tinnitus. The study was conducted on healthy young adults reporting bilateral continuous chronic tinnitus to limit the influence of age-related confounding factors and health-related comorbidities. METHODS: The present study evaluated the genome-wide methylation levels from saliva-derived DNA samples from 24 healthy young adults with bilateral continuous chronic tinnitus (> 1 year) and 24 age, sex, and ethnicity-matched controls with no tinnitus. Genome-wide DNA methylation was evaluated for > 850,000 CpG sites using the Infinium Human Methylation EPIC BeadChip. The association analysis used the Bumphunter algorithm on 23 cases and 20 controls meeting the quality control standards. The methylation level was expressed as the area under the curve of CpG sites within DMRs.The FDR-adjusted p-value threshold of 0.05 was used to identify statistically significant DMRs associated with tinnitus. RESULTS: We obtained 25 differentially methylated regions (DMRs) associated with tinnitus. Genes within or in the proximity of the hypermethylated DMRs related to tinnitus included LCLAT1, RUNX1, RUFY1, NUDT12, TTC23, SLC43A2, C4orf27 (STPG2), and EFCAB4B. Genes within or in the proximity of hypomethylated DMRs associated with tinnitus included HLA-DPB2, PM20D1, TMEM18, SNTG2, MUC4, MIR886, MIR596, TXNRD1, EID3, SDHAP3, HLA-DPB2, LASS3 (CERS3), C10orf11 (LRMDA), HLA-DQB1, NADK, SZRD1, MFAP2, NUP210L, TPM3, INTS9, and SLC2A14. The burden of genetic variation could explain the differences in the methylation levels for DMRs involving HLA-DPB2, HLA-DQB1, and MUC4, indicating the need for replication in large independent cohorts. CONCLUSION: Consistent with the literature on comorbidities associated with tinnitus, we identified genes within or close to DMRs involved in auditory functions, chemical dependency, cardiovascular diseases, psychiatric conditions, immune disorders, and metabolic syndromes. These results indicate that epigenetic mechanisms could influence tinnitus, and saliva can be a good surrogate for identifying the epigenetic underpinnings of tinnitus in humans. Further research with a larger sample size is needed to identify epigenetic biomarkers and investigate their influence on the phenotypic expression of tinnitus.

Unveiling potential drug targets for lung squamous cell carcinoma through the integration of druggable genome and genome-wide association data.

Background: Lung squamous cell carcinoma (LSCC) is a major subtype of lung cancer with poor prognosis and low survival rate. Compared with lung adenocarcinoma, yet no FDA-approved targeted-therapy has been found for lung squamous cell carcinoma. Methods: To identify potential drug targets for LSCC, Summary-data-based Mendelian randomization (SMR) analysis was used to examine the potential association between 4,543 druggable genes and LSCC, followed by colocalization analysis and HEIDI tests to confirm the robustness of the result. Phenome-wide association study (PheWAS) explored potential side effects of candidate drug targets. Enrichment analysis and protein-protein interaction networks revealed the function and significance of therapeutic targets. Single-cell expression analysis was used to examine cell types with enrichment expression of druggable genes in LSCC tissue. Drug prediction included screening potential drug candidates and evaluating their interactions with targets through molecular docking. Results: This research has identified ten significant drug targets for LSCC through a comprehensive SMR analysis. These targets included (COPA, PKD2L1, CCR1, C2, CYP21A2, and NCSTN as risk factors, and CCNA2, C4A, APOM, and LPAR2 as protective factors). PheWAS demonstrated that C2, CCNA2, LPAR2, and NCSTN exhibited associations with other phenotypes at the genetic level. Then, we found four potentially effective drugs with the Dsigdb database. Subsequently, molecular docking indicated that favorable binding interactions between drug candidates and potential target molecules. In the druggability evaluation, five out of ten drug target genes have been used in drug development (APOM, C4A, CCNA2, COPA, and PKD2L1). Six out of ten druggable genes showed significant expression in LSCC tissues (COPA, PKD2L1, CCR1, C2, NCSTN, LPAR2). Besides, Single-cell expression analysis revealed that C2 and CCNA2 were primarily enriched in macrophages, while COPA and NCSTN were enriched in both macrophages and epithelial cells. Conclusion: Our research revealed ten potential druggable genes for LSCC treatment, which might help to advance the precise and efficient therapeutic approaches of LSCC.

Haploid embryos and embryonic stem cells to produce offspring with predetermined parental genomes in cattle.

Selection strategies are performed post-fertilization when the random combination of paternal and maternal genomes has already occurred. It would be greatly advantageous to eliminate meiotic uncertainty by selecting genetically superior gametes before fertilization. To achieve this goal, haploid embryonic cells and embryonic stem cell lineages could be derived, genotyped, and used to substitute gametes. On the paternal side, androgenetic development can be achieved by removing the maternal chromosomes from the oocyte before or after fertilization. We have shown that once developed into an embryo, haploid cells can be removed for genotyping and, if carrying the selected genome, be used to replace sperm at fertilization. A similar strategy can be used on the maternal side by activating the oocyte parthenogenetically and using some embryonic cells for genotyping while the remaining are used to produce diploid embryos by fertilization. Placed together, both androgenetic and parthenogenetic haploid cells that have been genotyped to identify optimal genomes can be used to produce offspring with predetermined genomes. Successes and problems in developing such a breeding platform to achieve this goal are described and discussed below.

Exploring the full potential of sperm function with nanotechnology tools.

Sperm quality is essential to guarantee the success of assisted reproduction. However, selecting high-quality sperm and maintaining it during (cryo)preservation for high efficiency remains challenging in livestock reproduction. A comprehensive understanding of sperm biology allows for better assessment of sperm quality, which could replace conventional sperm analyses used today to predict fertility with low accuracy. Omics approaches have revealed numerous biomarkers associated with various sperm phenotypic traits such as quality, survival during storage, freezability, and fertility. At the same time, nanotechnology is emerging as a new biotechnology with high potential for use in preparing sperm intended to improve reproduction in livestock. The unique physicochemical properties of nanoparticles make them exciting tools for targeting (e.g., sperm damage and sexing) and non-targeting bioapplications. Recent advances in sperm biology have led to the discovery of numerous biomarkers, making it possible to target specific subpopulations of spermatozoa within the ejaculate. In this review, we explore potential biomarkers associated with sperm phenotypes and highlight the benefits of combining these biomarkers with nanoparticles to further improve sperm preparation and technology.

Prostate cancer genotyping for risk stratification and precision treatment.

Prostate cancer (PC) is the most frequently diagnosed cancer and second leading cause of cancer-related deaths in men. It is heterogeneous, as is evident from the wide spectrum of therapeutic approaches. Most patients with PC are initially responsive to androgen deprivation therapy; however, the majority of cases are either hormone-sensitive PC or castration-resistant PC. Current therapeutic protocols follow the evolution of PC, a continuously progressive process involving a combination of widespread genomic alterations. These genomic alterations are either hereditary germline mutations, such as mutations in BRCA2, or specific only to tumor cells (somatic). Tumor-specific genomic spectra include genomic structural rearrangements, canonical androgen response genes, and many other specific genes such as TMPRSS2-ERG fusion, SPOP/FOXA1, TP53/RB1/PTEN, and BRCA2. New evidence indicates the involvement of signaling pathways including PI3K, WNT/ß-catenin, SRC, and IL-6/STAT, which have been shown to promote epithelial-mesenchymal transition cancer stem cell-like features/stemness, and neuroendocrine differentiation in PC. Over the last decade, our understanding of the genotype-phenotype relationships has been enhanced considerably. The genetic background of PC related to canonical genetic alterations and signaling pathway activation genes has shed more insight into the molecular subtype and disease landscape, resulting in a more flexible role of individual therapies targeting diverse genotypes and phenotypes.

Use of Beacon v2 for Improving Genomics Based Research in a Clinical Setting.

GA4GH has proposed the Beacon architecture as an interface to retrieve genomic information which also protects the privacy of the individuals. In this paper, we propose to adapt the Beacon Reference Implementation to the use case of a study comparing the susceptibility to the carcinogenic effects of tobacco. This analysis compares the germline of heavy smokers who have either never developed lung cancer or, on the contrary, have developed it at a young age. To adapt the Beacon Reference Implementation to the use case, we have added filtering capabilities and a new grouping of information allowing to retrieve the data by affected gene.

The role of heterochronic gene expression and regulatory architecture in early developmental divergence.

New developmental programs can evolve through adaptive changes to gene expression. The annelid Streblospio benedicti has a developmental dimorphism, which provides a unique intraspecific framework for understanding the earliest genetic changes that take place during developmental divergence. Using comparative RNAseq through ontogeny, we find that only a small proportion of genes are differentially expressed at any time, despite major differences in larval development and life history. These genes shift expression profiles across morphs by either turning off any expression in one morph or changing the timing or amount of gene expression. We directly connect the contributions of these mechanisms to differences in developmental processes. We examine F1 offspring - using reciprocal crosses - to determine maternal mRNA inheritance and the regulatory architecture of gene expression. These results highlight the importance of both novel gene expression and heterochronic shifts in developmental evolution, as well as the trans-acting regulatory factors in initiating divergence.

Genomic analysis of "Candidatus Liberibacter africanus" strain from Zimbabwe reveals unique virulence and prophage characteristics compared to "Ca. Liberibacter asiaticus".

Citrus Huanglongbing (HLB) is caused by the phloem-limited α-proteobacterium "Candidatus Liberibacter spp.", among which "Ca. Liberibacter africanus" (CLaf) have posed a significant threat to citrus production in Africa near a century. CLaf is closely related to the globally prevalent "Ca. Liberibacter asiaticus" (CLas), whereas little is known about the virulence of CLaf, primarily due to limited genome resources. In this study, we completed the whole-genome assembly and annotation of CLaf strain Zim (from Zimbabwe). Compared to CLas, a total of 102 CLaf unique genes were identified, including 14 potential Sec-dependent effectors (SDEs) genes, 29 phage-associated genes, and 59 genes with hypothetical function. Among 14 SDEs, V9J15_03810 was able to induce a significant hypersensitive response (HR) in Nicotiana benthamiana, indicating its potential as a virulence factor for CLaf. Genome analysis showed that CLaf strain Zim genome harbored a complete prophage region (named P-Zim-1, 42,208 bp). P-Zim-1 retained two immunosuppressive peroxidase genes (V9J15_02125 and V9J15_02130) homologous to CLas prophage SC1/SC2, whereas the lysogen-associated genes encoding integrase (V9J15_01970) and repressor (V9J15_02080) were homologous to the prophage of "Ca. Liberibacter solanacearum", the causal agent of potato zebra chip disease. In addition, P-Zim-1 carried a novel CRISPR/Cas system, including a CRISPR array (located within V9J15_02040, ranging from 443,643 to 443,897) and five CRISPR-related Cas proteins (V9J15_02005, 02010, 02015, 02025 and 02035). This study first characterized the unique genomic feature of CLaf related to virulence and prophage, which will facilitate future research on CLaf biology and African HLB management.