Genomic Tools Research Articles

Exploring genetic diversity in apricot (Prunus armeniaca L.) populations using SSR markers

The genetic diversity of apricot (Prunus armeniaca L.) germplasm in mid-hills of Himachal Pradesh was investigated using Simple Sequence Repeats (SSR) markers. Apricot, a fruit-bearing tree known for its high nutritional value, requires genetic diversity analysis for effective breeding and conservation efforts. Molecular characterization using 46 SSR markers produced a total of 974 alleles with an average of 33.59 alleles per locus and PIC values ranging from 0.16 to 0.50, indicating a high level of polymorphism. PCR products of 15 indigenous collections and ‘Shakarpara’ revealed unique bands that were sequenced and subjected to BLAST, showing a high degree of homology with Prunus dulcis. A high quality reference genome for apricot is still unavailable, thereby limiting the elucidation of phenotypic associations with genetic background. Overall, this study provides valuable insights into the genetic diversity of apricot germplasm, crucial for sustainable breeding strategies, and need for integrating advanced genomic tools to further elucidate the genetic architecture of apricot.

Genomic Tools for Medicinal Properties of Goat Milk for Cosmetic and Health Benefits: A Narrative Review

Goat milk has gained recognition for its medicinal, cosmetic, and health benefits, particularly its potential to improve human skin conditions. Its therapeutic properties are attributed to bioactive compounds influenced by genes such as lactoferrin (LTF), lysozyme (LYZ), and β-casein (CSN2), known for their antimicrobial, immunomodulatory, and anti-inflammatory effects. Genetic factors are hypothesized to shape goat milk’s composition and its effectiveness in managing dermatological conditions like eczema and psoriasis. Understanding these genetic determinants is critical to optimizing the use of goat milk in skin health applications. This review aims to explore the application of genomic tools to elucidate the medicinal properties of goat milk and its implications for skin care. By identifying the specific genes and molecular mechanisms underpinning its therapeutic effects, genomic studies have provided insights into the bioactive constituents of goat milk, such as peptides, proteins, and lipids, which contribute to its dermatological efficacy. Candidate genes, including growth hormone receptor (GHR), butyrophilin (BTN1A1), and lactoglobulin (LGB), have been identified as critical for enhancing milk quality and functionality. Future research should integrate genomic data with functional studies to further investigate goat milk’s immunomodulatory, antimicrobial, and antioxidant activities. Such insights could advance targeted breeding strategies and innovative formulations for managing inflammatory skin conditions and promoting skin health.

Genome assemblies and other genomic tools for understanding insect adaptation.

Insects, the most diverse group of animals, exhibit remarkable adaptability, playing both crucial and problematic roles in ecosystems. Recent advancements in genomic technologies, such as high-throughput sequencing, have provided unprecedented insights into the genetic foundations of insect adaptation. This review explores key methodologies, including de novo and reference-guided genome assemblies, and highlights cutting-edge technologies like second and third-generation sequencing, and hybrid techniques. The article delves into the genetic mechanisms underlying insect adaptations, focusing on structural variants. Case studies, such as the Anopheles gambiae genome assembly and the genomic research on Drosophila melanogaster, demonstrate the practical applications of these technologies in understanding pesticide resistance, climate adaptation, and other evolutionary traits. This review underscores the transformative role of genomic tools in insect research, with significant implications for pest management, agriculture, and biodiversity conservation.

Application of Genomic Selection in Beef Cattle Disease Prevention.

Genomic applications in beef cattle disease prevention have gained traction in recent years, offering new strategies for improving herd health and reducing economic losses in the livestock industry. Advances in genomics, including identification of genetic markers linked to disease resistance, provide powerful tools for early detection, selection, and management of cattle resistant to infectious diseases. By incorporating genomic technologies such as whole-genome sequencing, genotyping, and transcriptomics, researchers can identify specific genetic variants associated with resistance to pathogens like bovine respiratory disease and Johne's disease. These genomic insights allow for more accurate breeding programs aimed at enhancing disease resistance and overall herd resilience. Genomic selection, in particular, enables identification of individuals with superior genetic traits for immune function, reducing the need for antibiotic treatments and improving animal welfare. Moreover, precision medicine, powered by genomic data, supports development of tailored health management strategies, including targeted vaccination plans and antimicrobial stewardship. Incorporation of genomic tools in beef cattle management also offers the potential for early disease detection, facilitating proactive interventions that reduce the spread of infections. Despite challenges like cost, data interpretation and integration into current management systems, the potential advantages of genomic applications in disease prevention are substantial. As these technologies advance, they are anticipated to have crucial roles in improving sustainability (by enhancing herd performance), profitability (by improving overall herd longevity), and biosecurity (by decreasing the likelihood of disease outbreaks) of beef cattle production systems worldwide.

Directed Evolution of Microbial Communities in Fermented Foods: Strategies, Mechanisms, and Challenges.

Directed Evolution of Microbial Communities (DEMC) offers a promising approach to enhance the functional attributes of microbial consortia in fermented foods by mimicking natural selection processes. This review details the application of DEMC in fermented foods, focusing on optimizing community traits to improve both fermentation efficiency and the sensory quality of the final products. We outline the core techniques used in DEMC, including the strategic construction of initial microbial communities, the systematic introduction of stress factors to induce desirable traits, and the use of artificial selection to cultivate superior communities. Additionally, we explore the integration of genomic tools and dynamic community analysis to understand and guide the evolutionary trajectories of these communities. While DEMC shows substantial potential for refining fermented food products, it faces challenges such as maintaining genetic diversity and functional stability of the communities. Looking ahead, the integration of advanced omics technologies and computational modeling is anticipated to significantly enhance the predictability and control of microbial community evolution in food fermentation processes. By systematically improving the selection and management of microbial traits, DEMC serves as a crucial tool for enhancing the quality and consistency of fermented foods, directly contributing to more robust and efficient food production systems.

GDBr: genomic signature interpretation tool for DNA double-strand break repair mechanisms.

Large genetic variants can be generated via homologous recombination (HR), such as polymerase theta-mediated end joining (TMEJ) or single-strand annealing (SSA). Given that these HR-based mechanisms leave specific genomic signatures, we developed GDBr, a genomic signature interpretation tool for DNA double-strand break repair mechanisms using high-quality genome assemblies. We applied GDBr to a draft human pangenome reference. We found that 78.1% of non-repetitive insertions and deletions and 11.0% of non-repetitive complex substitutions contained specific signatures. Of these, we interpreted that 98.7% and 1.3% of the insertions and deletions were generated via TMEJ and SSA, respectively, and all complex substitutions via TMEJ. Since population-level pangenome datasets are being dramatically accumulated, GDBr can provide mechanistic insights into how variants are formed. GDBr is available on GitHub at https://github.com/Chemical118/GDBr.

Single-cell Rapid Capture Hybridization sequencing to reliably detect isoform usage and coding mutations in targeted genes.

Single-cell long-read sequencing has transformed our understanding of isoform usage and the mutation heterogeneity between cells. Despite unbiased in-depth analysis, the low sequencing throughput often results in insufficient read coverage thereby limiting our ability to perform mutation calling for specific genes. Here, we developed a single-cell Rapid Capture Hybridization sequencing (scRaCH-seq) method that demonstrated high specificity and efficiency in capturing targeted transcripts using long-read sequencing, allowing an in-depth analysis of mutation status and transcript usage for genes of interest. The method includes creating a probe panel for transcript capture, using barcoded primers for pooling and efficient sequencing via Oxford Nanopore Technologies platforms. scRaCH-seq is applicable to stored and indexed single-cell cDNA which allows analysis to be combined with existing short-read RNA-seq datasets. In our investigation of BTK and SF3B1 genes in samples from patients with chronic lymphocytic leukaemia (CLL), we detected SF3B1 isoforms and mutations with high sensitivity. Integration with short-read scRNA-seq data revealed significant gene expression differences in SF3B1-mutated CLL cells, though it did not impact the sensitivity of the anti-cancer drug venetoclax. scRaCH-seq's capability to study long-read transcripts of multiple genes makes it a powerful tool for single-cell genomics.

Identification of Functionally-Relevant Lentivirus Integration Sites in an Insertional Mutagenesis Cell Library.

The extent of functional sequences within the human genome is a pivotal yet debated topic in biology. Although high-throughput reverse genetic screens have made strides in exploring this, they often limit their scope to known genomic elements and may introduce non-specific effects. This underscores the urgent need for novel functional genomics tools that enable a deeper, unbiased understanding of genome functionality. This protocol introduces the Insertion-based Screen for functional Elements and Transcripts (InSET), a method for identifying lentivirus integration sites within a lentivirus-based insertional mutagenesis cell library. InSET facilitates the capture of genome-wide lentiviral integration sites, with next-generation sequencing used to detect and quantify flanking sequences. InSET's design enables the analysis of integration site abundance variations in phenotypic screens on a large scale, establishing it as a robust tool for forward genetics and for identifying functional genomic elements. A key benefit of InSET is its capacity to reveal previously unidentified genomic elements, including novel functional exons of both protein-coding and non-coding RNAs, independent of prior annotation. Overall, InSET holds significant value in studying the intricate complexity of the human genome and transcriptome, where many genomic elements await functional characterization.

Targeted Enrichment Sequencing Utilizing a Respiratory Pathogen Panel for Genomic Wastewater-Based Viral Epidemiology in Uruguay.

Human respiratory and enteric viruses are responsible for substantial morbidity and mortality worldwide. Wastewater-based epidemiology utilizing next-generation sequencing serves as an effective tool for monitoring viral circulation dynamics at the community level. However, these complex environmental samples are often laden with other microorganisms and host genomic material, which can hinder the sensitivity of viral detection. To address this limitation, targeted enrichment sequencing is emerging as a preferred strategy, facilitating the acquisition of a more comprehensive understanding of specific pathogens. In this study, we evaluated the performance of a targeted enrichment sequencing panel for 42 excreted respiratory viruses (including Picornaviridae, Adenoviridae, Coronaviridae, Paramyxoviridae, Orthomyxoviridae, Orthoherpesviridae, Pneumoviridae, and Parvoviridae families), known as the Respiratory Pathogen ID/AMR enrichment panel (RPIP), coupled with Explify bioinformatics analysis in 3 sewage samples from Uruguay. RPIP panel successfully identified sequences from frequently circulating viruses, along with some that had not been documented previously. We identified and characterized various viruses, including human Enterovirus (Coxsackievirus A1 and A19), Influenza A-H1N1, and full-length sequences of SARS-CoV-2. Additionally, several other viral pathogens were detected, such as human Bocavirus, human Parechovirus, Enterovirus A71, and Enterovirus D68; however, for these viruses further analysis was limited due to the small genomic regions or low-read coverage obtained. While the RPIP panel necessitates substantial sequencing depth and may introduce bias towards the more predominant strains present in the samples, this approach suggests its viability as a genomic epidemiological tool for assessing respiratory and enteric viruses in wastewater.

Genomic Characterization of Carbapenemase-Producing Enterobacteriaceae from Clinical and Epidemiological Human Samples.

Background/Objectives: Infections caused by multidrug-resistant (MDR)bacteria pose a significant public health threat by worsening patient outcomes, contributing to hospital outbreaks, and increasing health and economic burdens. Advanced genomic tools enhance the detection of resistance genes, virulence factors, and high-risk clones, thus improving the management of MDR infections. In the Autonomous Community of Aragon, the diversity and incidence of carbapenemase-producing Enterobacteriaceae (CPE) have increased during the last years. This study analyses CPE trends at a tertiary hospital in Spain from 2021 to 2023, aiming to optimize personalized medicine. Methods: CPE isolates were the first isolate per patient, year, species, and carbapenemase from January 2021 to December 2023. Additional metadata were collected from the laboratory's information system. Antibiotic susceptibility testing was performed by broth microdilution. Whole-genome sequencing (WGS) was performed using Illumina short reads. De novo assembly was used to generate draft genomes in order to determine their complete taxonomic classification, resistome, plasmidome, sequence type (ST), core-genome multilocus sequence typing (cgMLST), and phylogenetic relationships using a suite of bioinformatics tools and in-house scripts. Results: Between 2021 and 2023, 0.4% out of 38,145 Enterobacteriaceae isolates were CPE. The CPE rate tripled in 2022 and doubled again in 2023. The most common species was Klebsiella pneumoniae (51.8%) and the most common carbapenemase was blaOXA-48. WGS revealed concordant species identification and the carbapenemase distribution in detail. Resistance rates to critical antibiotics, such as carbapenems, were variable, but in most cases were above 70%. Genetic diversity was observed in WGS and phylogenetic analyses, with plasmids often mediating carbapenemase dissemination. Conclusions: The increasing rate of CPE in healthcare settings highlights a critical public health challenge, with limited treatment options. Genomic characterization is essential to understanding resistance mechanisms, aiding therapy, limiting outbreaks, and improving precision medicine.

Advances in Whole Genome Sequencing: Methods, Tools, and Applications in Population Genomics.

With the rapid advancement of high-throughput sequencing technologies, whole genome sequencing (WGS) has emerged as a crucial tool for studying genetic variation and population structure. Utilizing population genomics tools to analyze resequencing data allows for the effective integration of selection signals with population history, precise estimation of effective population size, historical population trends, and structural insights, along with the identification of specific genetic loci and variations. This paper reviews current whole genome sequencing technologies, detailing primary research methods, relevant software, and their advantages and limitations within population genomics. The goal is to examine the application and progress of resequencing technologies in this field and to consider future developments, including deep learning models and machine learning algorithms, which promise to enhance analytical methodologies and drive further advancements in population genomics.

Molecular heterogeneity and MYC dysregulation in triple-negative breast cancer: genomic advances and therapeutic implications.

Triple-negative breast cancer (TNBC) is characterized by a diverse range of molecular features that have been extensively studied. MYC plays a critical role in regulating metabolism, differentiation, proliferation, cell growth, and apoptosis. Dysregulation of MYC is associated with poor prognosis and contributes to the development and progression of breast cancer. A particularly intriguing aspect of TNBC is its association with tumors in BRCA1 mutation carriers, especially in younger women. MYC may also contribute to resistance to adjuvant treatments. For TNBC, targeting MYC-regulated pathways in combination with inhibitors of other carcinogenic pathways offers a promising therapeutic approach. Several signaling pathways regulate TNBC, and targeting these pathways could lead to effective therapeutic strategies for breast cancer. Advances in genomic tools, such as CRISPR-Cas9, next-generation sequencing, and whole-exome sequencing, are revolutionizing breast cancer diagnoses. These technologies have significantly enhanced our understanding of MYC oncogenesis, particularly through CRISPR-Cas9 and NGS. Targeting MYC and its partner MAX could provide valuable insights into TNBC. Moreover, the therapeutic potential of targeting MYC-driven signaling mechanisms and their interactions with other oncogenic pathways, including PI3K/AKT/mTOR and Wnt/β-catenin, is increasingly recognized. Next-generation sequencing and CRISPR-Cas9 represent significant breakthroughs in genomic tools that open new opportunities to explore MYC's role in TNBC and facilitate the development of personalized treatment plans. This review discusses the future clinical applications of personalized treatment strategies for patients with TNBC.

Analytical Validation of the Labcorp Plasma Complete Test, a Cell-Free DNA Comprehensive Genomic Profiling Tool for Precision Oncology.

To help guide treatment decisions and clinical trial matching, tumor genomic profiling is an essential precision oncology tool. Liquid biopsy, a complementary approach to tissue testing, can assess tumor-specific DNA alterations circulating in the blood. Labcorp Plasma Complete is a next-generation sequencing, cell-free DNA comprehensive genomic profiling test that identifies clinically relevant somatic variants across 521 genes in advanced and metastatic solid cancers. Over 800 unique sequencing libraries across 27 cancer types were evaluated to establish analytical sensitivity, specificity, accuracy, and precision, reproducibility, and repeatability. Sensitivity was verified for each variant type, with a median variant allele frequency (VAF) of 1.25% and 1.27% for panel-wide single-nucleotide variants and insertions/deletions (sequence variants), respectively, with <1% VAF sensitivity observed for clinically actionable variants, 1.72-fold for copy number amplifications, 0.48% fusion read fraction for translocations, and 0.47% sequence mutation VAF for microsatellite instability-high. Analytical specificity was 99.9999% for single-nucleotide variants and 100% for all other variant types. Precision, reproducibility, and repeatability resulted in 94.9% average positive agreement and 99.9% average negative agreement for sequence variants and 100% average positive agreement and average negative agreement for copy number amplifications, translocations, and microsatellite instability. Orthogonal assays were used to assess accuracy, demonstrating an aggregate analytical concordance of 97.4% positive percentage agreement and >99.99997% negative percentage agreement for all variants. Overall, the test demonstrates high sensitivity, specificity, accuracy, and robustness to enable informed clinical decision-making.

Recent advances in synthetic biology toolkits and metabolic engineering of Ralstonia eutropha H16 for production of value-added chemicals.

Ralstonia eutropha H16, a facultative chemolithoautotrophic Gram-negative bacterium, demonstrates remarkable metabolic flexibility by utilizing either diverse organic substrates or CO2 as the sole carbon source, with H2 serving as the electron donor under aerobic conditions. The capacity of carbon and energy metabolism of R. eutropha H16 enabled development of synthetic biology technologies and strategies to engineer its metabolism for biosynthesis of value-added chemicals. This review firstly outlines the development of synthetic biology tools tailored for R. eutropha H16, including construction of expression vectors, regulatory elements, and transformation techniques. The availability of comprehensive omics data (i.e., transcriptomic, proteomic, and metabolomic) combined with the fully annotated genome sequence provides a robust genetic framework for advanced metabolic engineering. These advancements facilitate efficient reprogramming metabolic network of R. eutropha. The potential of R. eutropha as a versatile microbial platform for industrial biotechnology is further underscored by its ability to utilize a wide range of carbon sources for the production of value-added chemicals through both autotrophic and heterotrophic pathways. The integration of state-of-the-art genetic and genomic engineering tools and strategies with high cell-density fermentation processes enables engineered R. eutropha as promising microbial cell factories for optimizing carbon fluxes and expanding the portfolio of bio-based products.

Prediction of antimicrobial susceptibility of pneumococci based on whole-genome sequencing data: a direct comparison of two genomic tools to conventional antimicrobial susceptibility testing.

Determination of antimicrobial resistance (AMR) in pneumococcal isolates is important for surveillance purposes and in a clinical context. Antimicrobial susceptibility testing (AST) of pneumococci is complicated by the need for exact minimal inhibitory concentrations (MICs) of beta-lactam antibiotics. Two next-generation sequencing (NGS) analysis tools have implemented the prediction of AMR in their analysis workflow, including the prediction of MICs: Pathogenwatch (https://pathogen.watch/) and AREScloud (OpGen). The performance of these tools in comparison to phenotypic AST following EUCAST guidelines is unknown. A total of 538 Streptococcus pneumoniae isolates were used to compare both tools with phenotypic AST for penicillin, amoxicillin, cefotaxime/ceftriaxone, erythromycin, trimethoprim-sulfamethoxazole, and tetracycline. Disk diffusion was performed for all isolates, and broth microdilution was performed for isolates with reduced beta-lactam susceptibility. Demultiplexed FASTQ files from Illumina sequencing, covering the whole genome of pneumococci, were used as input for the NGS tools. Categorical agreement (CA), major error (ME), and very major error (VME) rates were calculated. For beta-lactam antibiotics, CA was high (>94%) associated with none or only one ME and VME (<1%). For erythromycin and tetracycline, CA was >93% for predictions by AREScloud, while for Pathogenwatch, this ranged around 88%. For trimethoprim-sulfamethoxazole, CA was for both tools <86%. High VME rates were observed for erythromycin and tetracycline, higher for Pathogenwatch (53.6% and 47.0%, respectively) compared to AREScloud (14.3% and 19.1%, respectively). Both tools performed excellently despite the complexity of predicting beta-lactam resistance in pneumococci. Further optimization and validation are needed for non-beta-lactams since high (very) major error rates were observed.

Trends in biotechnology: &lt;em&gt;Vibrio natriegens&lt;/em&gt; as potential micro-factory for valorization of crustacean waste

Vibrio natriegens has recently been identified as a promising host for the biotechnology industry thanks to its inherent qualities, which include its fast growth rate, non-pathogenicity to humans, and versatility in using substrates. These advantages have led to the potential use of V. natriegens in the biosynthesis of several products. Basically, the industrial scale requires fermentation or cultivation processes to be conducted at high substrate or biomass concentrations to maximize the final retrieved product. However, studies on V. natriegens at high cell density are limited. Besides, the potential of V. natriegens to convert recalcitrant substrates such as chitin derivatives into biological products has not yet been understood. This review summarizes up-to-date information on the physiological characteristics, metabolism, genome, and genetic modification tools of V. natriegens. Subsequentially, statistics and analysis of research trends related to V. natriegens was presented. Finally, a discussion on the role of V. natriegens in converting chitin waste from the seafood processing industry into a culturing feedstock to achieve a circular economy and net zero emissions was provided.

A population genomics approach to unlock the genetic potential of lablab (Lablab purpureus (L.) Sweet), an underutilized tropical forage crop

BackgroundLablab is one of the conventionally grown multi-purpose crops that originated in Africa. It is an annual or short-lived perennial forage legume which has versatile uses (as a vegetable and dry seeds, as food or feed, or as green manure) but is yet to receive adequate research attention and hence remains underexploited. To develop new and highly productive lablab varieties, using genomics-assisted selection, the present study aimed to identify quantitative trait loci associated with agronomically important traits in lablab and to assess the stability of these traits across two different agro-ecologies in Ethiopia. Here, we resequenced one hundred and forty-two lablab accessions, utilised whole genome genotyping approaches, and conducted multi-locational phenotyping over two years.ResultsThe selected lablab accessions displayed significant agro-morphological variation in eight analysed traits, including plant height, total fresh weight, and total dry weight. Furthermore, the agronomic performance of the accessions was significantly different across locations and years, highlighting substantial genotype-by-environment interactions. The population genetic structure of the lablab accessions, based on ~ 500,000 informative single nucleotide polymorphisms (SNPs), revealed an independent domestication pattern for two-seeded and four-seeded lablab accessions. Finally, based on multi-environmental trial data, a genome-wide association study (GWAS) identified useful SNPs and k-mers for key traits, such as plant height and total dry weight.ConclusionsThe publicly available genomic tools and field evaluation data from this study will offer a valuable resource for plant breeders and researchers to inform a new cycle of lablab breeding. With the aid of these tools, the breeding cycle will be significantly reduced and livestock farmers will have access to improved lablab varieties in a shorter time-frame.

Integrating Bioinformatics and Genomic Tools for Sustainable Crop Improvement: A Review

Sustainable crop improvement is a crucial endeavor that aims to develop crops capable of maintaining or increasing yields while minimizing negative environmental impacts and adapting to the challenges posed by climate change. As the global population continues to grow, and agricultural land becomes increasingly limited, there is an urgent need for innovative and efficient approaches to enhance crop productivity and ensure food security. In this context, the integration of bioinformatics and molecular breeding has emerged as a powerful tool for sustainable crop improvement. Bioinformatics plays a pivotal role in addressing the challenges of sustainable crop improvement by enabling the efficient analysis and interpretation of vast amounts of genomic data. Advanced bioinformatics tools and resources allow researchers to mine and exploit the wealth of information generated by high-throughput sequencing technologies, facilitating the identification of key genes, molecular markers, and regulatory networks associated with desirable agronomic traits. By leveraging these insights, breeders can develop targeted strategies for crop improvement that are both efficient and environmentally sustainable. The application of bioinformatics in sustainable crop improvement has yielded significant successes in various areas. For instance, bioinformatics approaches have been instrumental in identifying genes conferring resistance to abiotic stresses such as drought, salinity, and extreme temperatures, as well as biotic stresses caused by pests and pathogens. By incorporating these resistance genes into elite crop varieties through molecular breeding techniques like marker-assisted selection and genome editing, researchers have developed crops that are more resilient to environmental challenges, thereby reducing the need for chemical inputs and promoting sustainable agriculture. Moreover, bioinformatics has facilitated the development of nutrient-efficient crops that require fewer fertilizers, thus minimizing the environmental impact of agriculture. Genomic data analysis has also enabled the identification of genes controlling yield and quality traits, allowing for the creation of crops with enhanced nutritional value and improved agronomic performance. Additionally, bioinformatics has played a crucial role in the conservation and utilization of genetic diversity in crop breeding programs, ensuring the long-term sustainability of agricultural systems. he integration of bioinformatics and molecular breeding techniques has revolutionized the field of sustainable crop improvement. By harnessing the power of genomic data and applying precise breeding approaches, researchers can develop crops that are better suited to the challenges of a changing climate and the needs of a growing population. This synergistic application of bioinformatics and molecular breeding holds immense potential for enhancing agricultural productivity, reducing environmental impacts, and ultimately contributing to global food security in a sustainable manner.

Recent Technological Advancements for Identifying and Exploiting Novel Sources of Pest and Disease Resistance for Peanut Improvement

Peanut, also known as groundnut (Arachis hypogaea L.), is an important oilseed and food crop globally, contributing significantly to the economy and food security. However, its productivity is often hampered by pests and diseases. Traditional breeding methods have been used to develop resistant cultivars, but these are often time-consuming and labor-intensive. Recent technological advancements have revolutionized the identification of novel resistance sources and the development of resistant peanut cultivars. This review explores the latest techniques and approaches used in peanut breeding for pest and disease resistance, focusing on the identification of resistance loci and their incorporation into peanut using marker-assisted selection (MAS) and genomic tools. Next-generation sequencing (NGS) technologies, bioinformatics pipelines, comparative genomics, and transcriptomics have helped identify a plethora of candidate genes involved in pest resistance. However, peanut lags behind other cereal crops in terms of phenomics and precision genetic techniques for their functional validation. In conclusion, recent technological advancements have significantly improved the efficiency and precision of peanut breeding for pest and disease resistance and hold great promise for developing durable and sustainable resistance in peanut cultivars, ultimately benefiting peanut farmers and consumers globally.

Insights into ecology, pathogenesis, and biofilm formation of Enterococcus faecalis from functional genomics.

SUMMARYEnterococcus faecalis is a significant resident of the gastrointestinal tract of most animals, including humans. Although generally non-pathogenic in healthy hosts, this microbe is adept at the exploitation of compromises in host immune functions, resulting in life-threatening opportunistic infections whose treatments are complicated by a high degree of intrinsic and acquired resistance to antimicrobial chemotherapy. Historically, progress in enterococcal research was limited by a lack of experimental models that replicate natural infection pathways and the relevance of in vitro studies to the natural biology of the organism. In this review, we summarize the history of enterococcal research during the 20th and early 21st centuries and describe more recent genetic and genomic tools and screens developed to address challenges in the field. We also describe how the results of recent studies reveal the importance of previously uncharacterized enterococcal genes, and we provide examples of interesting determinants that have emerged as important contributors to enterococcal biology. These factors may also serve as targets for future vaccines and chemotherapeutic agents to combat life-threatening hospital infections.