Genomics-based Approaches Research Articles

Genomics—based approaches may assist in the verification and accelerate responsible deployment of marine carbon dioxide removal

Rapid development and deployment of marine carbon dioxide removal (mCDR) approaches will be required to prevent the worst consequences of climate change and meet national treaty obligations under the Paris agreement. However, approaches to monitor the efficacy and environmental safety of mCDR are not being developed with the same intensity as the technology. Verification will be required to convince a sceptical public and regulatory community of the overall benefit of mCDR as well as provide the regulatory community a basis for risk assessments that will be required for at scale deployments. In this perspective, we posit that genomics-based approaches can be used to assess the efficacy of carbon sequestration and monitor for the possibility of unintended consequences. By adopting these approaches, it will be feasible to develop the evidence portfolio necessary to underpin assessments of the risks, benefits and trade-offs involved in responsible deployment of mCDR.

Redox-Active Cerium Dioxide Nanoparticles for Mitigating Anthracene Contamination: Promising Solution to Polycyclic Aromatic Hydrocarbon Remediation in Stormwater-Affected Soils

Due to the pressing environmental issue of polycyclic aromatic hydrocarbon (PAH) contamination in stormwater, top surface soil amendment by rare-earth-based nanoparticles was investigated as an effective remediation strategy. Here, the amendment of cerium dioxide (CeO2) nanoparticles (CeO2-NPs) into topsoil were studied to understand their role in mitigating anthracene contamination. Leveraging the redox activity of CeO2-NPs, this research demonstrates their capability to adsorb/degrade anthracene, thereby reducing its bioavailability and mobility within the soil. X-ray photoelectron spectroscopy (XPS) and Energy Dispersive X-ray Spectroscopy (EDS) analyses confirmed the homogeneous distribution of CeO2-NPs and revealed the altered chemical states of carbon, indicative of interaction with anthracene and the soil matrix. Further adsorption studies and soil column tests of four concentrations of anthracene (0, 5, 10, 15mg/L) revealed their adsorption onto CeO2-NPs. Further, size distribution, zeta potentials, and TEM images documented the efficiency of this strategy by over 60%. The role of soil organic matter in the presence of anthracene was further investigated to understand their reciprocal impact on the physiochemical properties of CeO2-NPs. We extended this analysis by incorporating genomics-based approaches, assessing alterations in community taxonomic structure and function through 16S rDNA profiling and heatmap of microbial diversity as affected by NPs or anthracene. This study enhances our understanding of nanotechnology's potential for environmental remediation of contaminated stormwater run-off.

Revolutionising Neurological Therapeutics: Investigating Drug Repurposing Strategies.

Repurposing drugs (DR) has become a viable approach to hasten the search for cures for neurodegenerative diseases (NDs). This review examines different off-target and on-target drug discovery techniques and how they might be used to find possible treatments for non-diagnostic depressions. Off-target strategies look at the known or unknown side effects of currently approved drugs for repositioning, whereas on-target strategies connect disease pathways to targets that can be treated with drugs. The review highlights the potential of experimental and computational methodologies, such as machine learning, proteomic techniques, network and genomics-based approaches, and in silico screening, in uncovering new drug-disease correlations. It also looks at difficulties and failed attempts at drug repurposing for NDs, highlighting the necessity of exact and standardised procedures to increase success rates. This review's objectives are to address the purpose of drug repurposing in human disorders, particularly neurological diseases, and to provide an overview of repurposing candidates that are presently undergoing clinical trials for neurological conditions, along with any possible causes and early findings. We then include a list of drug repurposing strategies, restrictions, and difficulties for upcoming research.

Global Analysis of Natural Products Biosynthetic Diversity Encoded in Fungal Genomes.

Fungal secondary metabolites (SMs) represent an invaluable source of therapeutic drugs. Genomics-based approaches to SM discovery have revealed a vast and largely untapped biosynthetic potential within fungal genomes. Here, we used the publicly available fungal genome sequences from the NCBI public database, as well as tools such as antiSMASH, BIG-SLiCE, etc., to analyze a total of 11,598 fungal genomes, identifying 293,926 biosynthetic gene clusters (BGCs), which were subsequently categorized into 26,825 gene cluster families (GCFs). It was discovered that only a tiny fraction, less than 1%, of these GCFs could be mapped to known natural products (NPs). Some GCFs that only contain a single BGC internally are crucial for the biodiversity of fungal biosynthesis. Evident patterns emerged from our analysis, revealing popular taxa as prominent sources of both actual and potential biosynthetic diversity. Our study also suggests that the genus rank distribution of GCF is generally consistent with NP diversity. It is noteworthy that genera Xylaria, Hypoxylon, Colletotrichum, Diaporthe, Nemania, and Calonectria appear to possess a higher potential for SM synthesis. In addition, 7213 BGCs match possible known compound structures, and homologous gene clusters of well-known drugs can be located in different genera, facilitating the development of derivatives that share structural similarity to these drugs and may potentially possess similar biological activity. Our study demonstrated the various types of fungi with mining potential, assisting researchers in prioritizing their research efforts and avoiding duplicate mining of known resources to further explore fungal NP producers.

Genomic prediction and genome-wide association study using combined genotypic data from different genotyping systems: application to apple fruit quality traits.

With advances in next-generation sequencing technologies, various marker genotyping systems have been developed for genomics-based approaches such as genomic selection (GS) and genome-wide association study (GWAS). As new genotyping platforms are developed, data from different genotyping platforms must be combined. However, the potential use of combined data for GS and GWAS has not yet been clarified. In this study, the accuracy of genomic prediction (GP) and the detection power of GWAS increased for most fruit quality traits of apples when using combined data from different genotyping systems, Illumina Infinium single-nucleotide polymorphism array and genotyping by random amplicon sequencing-direct (GRAS-Di) systems. In addition, the GP model, which considered the inbreeding effect, further improved the accuracy of the seven fruit traits. Runs of homozygosity (ROH) islands overlapped with the significantly associated regions detected by the GWAS for several fruit traits. Breeders may have exploited these regions to select promising apples by breeders, increasing homozygosity. These results suggest that combining genotypic data from different genotyping platforms benefits the GS and GWAS of fruit quality traits in apples. Information on inbreeding could be beneficial for improving the accuracy of GS for fruit traits of apples; however, further analysis is required to elucidate the relationship between the fruit traits and inbreeding depression (e.g. decreased vigor).

Harnessing the potential of reverse-phase protein array technology: Advancing precision oncology strategies.

The last few decades have seen remarkable strides in the field of cancer therapy. Precision oncology coupled with comprehensive genomic profiling has become routine clinical practice for solid tumors, the advent of immune checkpoint inhibitors has transformed the landscape of oncology treatment, and the number of cancer drug approvals has continued to increase. Nevertheless, the application of genomics-driven precision oncology has thus far benefited only 10%-20% of cancer patients, leaving the majority without matched treatment options. This limitation underscores the need to explore alternative avenues with regard to selecting patients for targeted therapies. In contrast with genomics-based approaches, proteomics-based strategies offer a more precise understanding of the intricate biological processes driving cancer pathogenesis. This perspective underscores the importance of integrating complementary proteomic analyses into the next phase of precision oncology to establish robust biomarker-drug associations and surmount challenges related to drug resistance. One promising technology in this regard is the reverse-phase protein array (RPPA), which excels in quantitatively detecting protein modifications, even with limited amounts of sample. Its cost-effectiveness and rapid turnaround time further bolster its appeal for application in clinical settings. Here, we review the current status of genomics-driven precision oncology, as well as its limitations, with an emphasis on drug resistance. Subsequently, we explore the application of RPPA technology as a catalyst for advancing precision oncology. Through illustrative examples drawn from clinical trials, we demonstrate its utility for unraveling the molecular mechanisms underlying drug responses and resistance.

Utilizing immunogenomic approaches to prioritize targetable neoantigens for personalized cancer immunotherapy.

Advancements in sequencing technologies and bioinformatics algorithms have expanded our ability to identify tumor-specific somatic mutation-derived antigens (neoantigens). While recent studies have shown neoantigens to be compelling targets for cancer immunotherapy due to their foreign nature and high immunogenicity, the need for increasingly accurate and cost-effective approaches to rapidly identify neoantigens remains a challenging task, but essential for successful cancer immunotherapy. Currently, gene expression analysis and algorithms for variant calling can be used to generate lists of mutational profiles across patients, but more care is needed to curate these lists and prioritize the candidate neoantigens most capable of inducing an immune response. A growing amount of evidence suggests that only a handful of somatic mutations predicted by mutational profiling approaches act as immunogenic neoantigens. Hence, unbiased screening of all candidate neoantigens predicted by Whole Genome Sequencing/Whole Exome Sequencing may be necessary to more comprehensively access the full spectrum of immunogenic neoepitopes. Once putative cancer neoantigens are identified, one of the largest bottlenecks in translating these neoantigens into actionable targets for cell-based therapies is identifying the cognate T cell receptors (TCRs) capable of recognizing these neoantigens. While many TCR-directed screening and validation assays have utilized bulk samples in the past, there has been a recent surge in the number of single-cell assays that provide a more granular understanding of the factors governing TCR-pMHC interactions. The goal of this review is to provide an overview of existing strategies to identify candidate neoantigens using genomics-based approaches and methods for assessing neoantigen immunogenicity. Additionally, applications, prospects, and limitations of some of the current single-cell technologies will be discussed. Finally, we will briefly summarize some of the recent models that have been used to predict TCR antigen specificity and analyze the TCR receptor repertoire.

Genomics-based taxonomy to clarify cannabis classification.

In the 18th century, Carolus Linnaeus created a formalized system of classification of living organisms based on their anatomic relationships, which we know as taxonomic nomenclature. Historically, the genus Cannabis has been described three ways under this system: Cannabis sativa by C. Linnaeus in 1753, Cannabis indica by J.B. Lamarck in 1785, and Cannabis ruderalis by D.E. Janischewsky in 1924, with these taxonomic classifications having been derived from physical, morphological, chemical, and geographical data. Today, this confusing taxonomy has led to an ongoing debate about whether the genus Cannabis consists of a single species or multiple distinct species or subspecies. Recently, genome sequencing and bioinformatics have provided greater resolution of taxonomic assignments at the species level. As a result, some previously discussed classification frameworks have been brought into question. The aim of this review is to provide a historical context for the confusion surrounding the taxonomy of the genus Cannabis and highlight recent research on genomics-based taxonomical approaches to clarify the question of Cannabis taxonomy. We suggest that the latest evidence shifts away from the previous multiple species framework and points towards the genus Cannabis consisting of a highly diverse monotypic species.

PUREE: accurate pan-cancer tumor purity estimation from gene expression data

Tumors are complex masses composed of malignant and non-malignant cells. Variation in tumor purity (proportion of cancer cells in a sample) can both confound integrative analysis and enable studies of tumor heterogeneity. Here we developed PUREE, which uses a weakly supervised learning approach to infer tumor purity from a tumor gene expression profile. PUREE was trained on gene expression data and genomic consensus purity estimates from 7864 solid tumor samples. PUREE predicted purity with high accuracy across distinct solid tumor types and generalized to tumor samples from unseen tumor types and cohorts. Gene features of PUREE were further validated using single-cell RNA-seq data from distinct tumor types. In a comprehensive benchmark, PUREE outperformed existing transcriptome-based purity estimation approaches. Overall, PUREE is a highly accurate and versatile method for estimating tumor purity and interrogating tumor heterogeneity from bulk tumor gene expression data, which can complement genomics-based approaches or be used in settings where genomic data is unavailable.

Abstract NG02: Ordered and selective patterns of cancer genome evolution revealed by lineage tracing of sporadic p53 loss

Abstract Inactivation of the tumor suppressor p53 is associated with molecular features such as genomic instability, polyploidy, and the emergence of heterogeneous tumors that acquire diverse copy number alterations (CNAs). Clinically, p53 loss is associated with aggressive disease, therapeutic resistance, and poor prognosis. However, precisely when specific molecular features are selected following p53 inactivation and their evolutionary contribution to the initiation and progression of heterogenous p53 mutant tumors, remains poorly understood. This is in part because cancer genomes are typically analyzed after tumor development and not as they transverse the benign to malignant spectrum. Here, we develop a mouse model of pancreatic ductal adenocarcinoma (PDAC) that reports sporadic p53 loss in vivo, irrespective of phenotype, enabling the identification, isolation and lineage tracing of incipient cancer cells as they evolve through the benign to malignant transition. Using functional, histopathological, and genomics-based approaches, we find that cells undergoing p53 loss gradually gain malignant potential as they acquire CNAs in highly ordered and selective patterns. Initially, distinct p53-deficient clones emerge and compete via the selective acquisition of recurrent chromosomal deletions in otherwise diploid cells. Following the accumulation of many genomic deletions in a diploid state, a subset of highly rearranged clones undergo genome doubling, entering polyploid, presumably an adaptation to excess loss of gene dosage. In a polyploid state, rampant genomic diversification ensues involving the acquisition of additional deletions and, now, widespread chromosomal gains and amplifications resulting in a heterogenous malignant tumor. Despite vast heterogeneity, chromosomal deletions are selectively fixed as truncal, homogenous events in the tumor hierarchy. Retrospective analysis of whole genome, targeted-panel, and single-cell sequencing data of human PDAC corroborate these patterns in human disease. p53 was coined “guardian of the genome” 30 years ago upon the discovery that its inactivation prevents proliferative arrest following DNA damage, leading to genomic instability. However, the evolutionary relationship between p53 inactivation, associated genomic features, and the emergence of p53 tumors remains poorly understood. Leveraging a unique lineage tracing mouse model, we find that p53 inactivation results in a set of predictable evolutionary rules that are highly ordered and deterministic. These specific rules suggest stage dependent contributions of CNA events to malignant transformation and point to new strategies to target lethal, p53-deficient tumors. Citation Format: Timour Baslan. Ordered and selective patterns of cancer genome evolution revealed by lineage tracing of sporadic p53 loss. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr NG02.

Recent Advances in Genomics-Based Approaches for the Development of Intracellular Bacterial Pathogen Vaccines.

Infectious diseases continue to be a leading cause of morbidity and mortality worldwide. The majority of infectious diseases are caused by intracellular pathogenic bacteria (IPB). Historically, conventional vaccination drives have helped control the pathogenesis of intracellular bacteria and the emergence of antimicrobial resistance, saving millions of lives. However, in light of various limitations, many diseases that involve IPB still do not have adequate vaccines. In response to increasing demand for novel vaccine development strategies, a new area of vaccine research emerged following the advent of genomics technology, which changed the paradigm of vaccine development by utilizing the complete genomic data of microorganisms against them. It became possible to identify genes related to disease virulence, genetic patterns linked to disease virulence, as well as the genetic components that supported immunity and favorable vaccine responses. Complete genomic databases, and advancements in transcriptomics, metabolomics, structural genomics, proteomics, immunomics, pan-genomics, synthetic genomics, and population biology have allowed researchers to identify potential vaccine candidates and predict their effects in patients. New vaccines have been created against diseases for which previously there were no vaccines available, and existing vaccines have been improved. This review highlights the key issues and explores the evolution of vaccines. The increasing volume of IPB genomic data, and their application in novel genome-based techniques for vaccine development, were also examined, along with their characteristics, and the opportunities and obstacles involved. Critically, the application of genomics technology has helped researchers rapidly select and evaluate candidate antigens. Novel vaccines capable of addressing the limitations associated with conventional vaccines have been developed and pressing healthcare issues are being addressed.

Immunoinformatic-guided designing of multi-epitope vaccine construct against Brucella Suis 1300.

Brucella suis mediates the transmission of brucellosis in humans and animals and a significant facultative zoonotic pathogen found in livestock. It has the capacity to survive and multiply in a phagocytic environment and to acquire resistance under hostile conditions thus becoming a threat globally. Antibiotic resistance is posing a substantial public health threat, hence there is an unmet and urgent clinical need for immune-based non-antibiotic methods to treat brucellosis. Hence, we aimed to explore the whole proteome of Brucella suis to predict antigenic proteins as a vaccine target and designed a novel chimeric vaccine (multi-epitope vaccine) through subtractive genomics-based reverse vaccinology approaches. The applied subsequent hierarchical shortlisting resulted in the identification of Multidrug efflux Resistance-nodulation-division (RND) transporter outer membrane subunit (gene BepC) that may act as a potential vaccine target. T-cell and B-cell epitopes have been predicted from target proteins using a number of immunoinformatic methods. Six MHC I, ten MHC II, and four B-cell epitopes were used to create a 324-amino-acid MEV construct, which was coupled with appropriate linkers and adjuvant. To boost the immunological response to the vaccine, the vaccine was combined with the TLR4 agonist HBHA protein. The MEV structure predicted was found to be highly antigenic, non-toxic, non-allergenic, flexible, stable, and soluble. To confirm the interactions with the receptors, a molecular docking simulation of the MEV was done using the human TLR4 (toll-like receptor 4) and HLAs. The stability and binding of the MEV-docked complexes with TLR4 were assessed using molecular dynamics (MD) simulation. Finally, MEV was reverse translated, its cDNA structure was evaluated, and then, in silico cloning into an E. coli expression host was conducted to promote maximum vaccine protein production with appropriate post-translational modifications. These comprehensive computer calculations backed up the efficacy of the suggested MEV in protecting against B. suis infections. However, more experimental validations are needed to adequately assess the vaccine candidate's potential. HIGHLIGHTS: • Subtractive genomic analysis and reverse vaccinology for the prioritization of novel vaccine target • Examination of chimeric vaccine in terms of allergenicity, antigenicity, MHC I, II binding efficacy, and structural-based studies • Molecular docking simulation method to rank based vaccine candidate and understand their binding modes.

Targeted Large-Scale Genome Mining and Candidate Prioritization for Natural Product Discovery.

Large-scale genome-mining analyses have identified an enormous number of cryptic biosynthetic gene clusters (BGCs) as a great source of novel bioactive natural products. Given the sheer number of natural product (NP) candidates, effective strategies and computational methods are keys to choosing appropriate BGCs for further NP characterization and production. This review discusses genomics-based approaches for prioritizing candidate BGCs extracted from large-scale genomic data, by highlighting studies that have successfully produced compounds with high chemical novelty, novel biosynthesis pathway, and potent bioactivities. We group these studies based on their BGC-prioritization logics: detecting presence of resistance genes, use of phylogenomics analysis as a guide, and targeting for specific chemical structures. We also briefly comment on the different bioinformatics tools used in the field and examine practical considerations when employing a large-scale genome mining study.

Potential utility of reverse-phase protein array for refining precision cancer medicine

Rapid advances in next-generation sequencing (NGS) technology over the past decade have led to a paradigm shift in cancer treatment from one-size-fits-all medicine to genomics-based precision medicine that has become more accessible in general clinical practice. It, however, has helped only less than 10% of the cancer patients who have taken genomic profiling tests. Proteomics-based rather than genomics-based approaches can capture biological processes directly contributing to cancer pathogenesis. There is, therefore, a growing need for simultaneous proteomic analysis in the next stage of precision medicine. In the future, it is expected that data from genomic, transcriptomic, proteomic, and epigenomic analyses will be integrated and complemented to more accurately understand the disease states and propose therapeutic strategies. Reverse-phase protein array (RPPA) is a proteomic technology that enables semi-quantitative, accurate, and high throughput signal profiling even in small amounts of samples. This review introduces its current status and the application to precision cancer medicine, and highlights the potential utility of RPPA technology for improving precision cancer medicine.

Microalgal fuels: Promising energy reserves for the future

Traditional methods of fuel production and consumption result in the emission of huge quantities of CO2, leading to a rise in the Earth’s surface temperature. This has driven the scientific community to look for alternate sources of fuels that could fulfil the current energy demands, while reducing CO2 emissions. Microalgae have garnered substantial interest as an excellent biological resource for biorefineries, given their abilities to accumulate diverse CO2-derived bioproducts, such as various forms of fuels, chemical building blocks, and pharmaceuticals; this could be interpreted as an efficient method for storing solar energy by biological means. Microalgal biomass is free from ethical debates because it does not compete with food production for fertile arable lands and clean water for their cultivation, which makes the entire process highly sustainable. Microalgal biomass-derived fuels for each end-use can be obtained via several processes, including drying, chemical, and physical lipid-extraction methods, anaerobic digestion, and thermochemical treatments (e.g., pyrolysis, torrefaction, hydrothermal carbonization, liquefaction, and gasification). This review aims to critically explore the process layouts, their operation conditions (including catalysts), and expected outputs related to fuel production. It also provides a comprehensive overview of the most notable and promising strategies for the producing energy resources from various species of microalgae. Pretreatment of microalgal biomass, followed by bioconversion strategies, such as hydrothermal carbonization and photo-fermentation, along with genomics-based approaches, can lead to higher yields of microalga-derived solid, liquid, and gaseous fuels.

Human Genomics and Drug Development.

Insights into the genetic basis of human disease are helping to address some of the key challenges in new drug development including the very high rates of failure. Here we review the recent history of an emerging, genomics-assisted approach to pharmaceutical research and development, and its relationship to Mendelian randomization (MR), a well-established analytical approach to causal inference. We demonstrate how human genomic data linked to pharmaceutically relevant phenotypes can be used for (1) drug target identification (mapping relevant drug targets to diseases), (2) drug target validation (inferring the likely effects of drug target perturbation), (3) evaluation of the effectiveness and specificity of compound-target engagement (inferring the extent to which the effects of a compound are exclusive to the target and distinguishing between on-target and off-target compound effects), and (4) the selection of end points in clinical trials (the diseases or conditions to be evaluated as trial outcomes). We show how genomics can help identify indication expansion opportunities for licensed drugs and repurposing of compounds developed to clinical phase that proved safe but ineffective for the original intended indication. We outline statistical and biological considerations in using MR for drug target validation (drug target MR) and discuss the obstacles and challenges for scaled applications of these genomics-based approaches.

Advanced Genomics-Based Approaches for Defining Allograft Rejection With Single Cell Resolution.

Solid organ transplant recipients require long-term immunosuppression for prevention of rejection. Calcineurin inhibitor (CNI)-based immunosuppressive regimens have remained the primary means for immunosuppression for four decades now, yet little is known about their effects on graft resident and infiltrating immune cell populations. Similarly, the understanding of rejection biology under specific types of immunosuppression remains to be defined. Furthermore, development of innovative, rationally designed targeted therapeutics for mitigating or preventing rejection requires a fundamental understanding of the immunobiology that underlies the rejection process. The established use of microarray technologies in transplantation has provided great insight into gene transcripts associated with allograft rejection but does not characterize rejection on a single cell level. Therefore, the development of novel genomics tools, such as single cell sequencing techniques, combined with powerful bioinformatics approaches, has enabled characterization of immune processes at the single cell level. This can provide profound insights into the rejection process, including identification of resident and infiltrating cell transcriptomes, cell-cell interactions, and T cell receptor α/β repertoires. In this review, we discuss genomic analysis techniques, including microarray, bulk RNAseq (bulkSeq), single-cell RNAseq (scRNAseq), and spatial transcriptomic (ST) techniques, including considerations of their benefits and limitations. Further, other techniques, such as chromatin analysis via assay for transposase-accessible chromatin sequencing (ATACseq), bioinformatic regulatory network analyses, and protein-based approaches are also examined. Application of these tools will play a crucial role in redefining transplant rejection with single cell resolution and likely aid in the development of future immunomodulatory therapies in solid organ transplantation.

Fine Sampling of Sequence Space for Membrane Protein Structural Biology

We describe an enhancement of traditional genomics-based approaches to improve the success of structure determination of membrane proteins. Following a broad screen of sequence space to identify initial expression-positive targets, we employ a second step to select orthologs with closely related sequences to these hits. We demonstrate that a greater percentage of these latter targets express well and are stable in detergent, increasing the likelihood of identifying candidates that will ultimately yield structural information.

Unbiased interrogation of memory B cells from convalescent COVID-19 patients reveals a broad antiviral humoral response targeting SARS-CoV-2 antigens beyond the spike protein

Patients who recover from SARS-CoV-2 infections produce antibodies and antigen-specific T cells against multiple viral proteins. Here, an unbiased interrogation of the anti-viral memory B cell repertoire of convalescent patients has been performed by generating large, stable hybridoma libraries and screening thousands of monoclonal antibodies to identify specific, high-affinity immunoglobulins (Igs) directed at distinct viral components. As expected, a significant number of antibodies were directed at the Spike (S) protein, a majority of which recognized the full-length protein. These full-length Spike specific antibodies included a group of somatically hypermutated IgMs. Further, all but one of the six COVID-19 convalescent patients produced class-switched antibodies to a soluble form of the receptor-binding domain (RBD) of S protein. Functional properties of anti-Spike antibodies were confirmed in a pseudovirus neutralization assay. Importantly, more than half of all of the antibodies generated were directed at non-S viral proteins, including structural nucleocapsid (N) and membrane (M) proteins, as well as auxiliary open reading frame-encoded (ORF) proteins. The antibodies were generally characterized as having variable levels of somatic hypermutations (SHM) in all Ig classes and sub-types, and a diversity of VL and VH gene usage. These findings demonstrated that an unbiased, function-based approach towards interrogating the COVID-19 patient memory B cell response may have distinct advantages relative to genomics-based approaches when identifying highly effective anti-viral antibodies directed at SARS-CoV-2.

Current State and Future Directions of Genetics and Genomics of Endophytic Fungi for Bioprospecting Efforts.

The bioprospecting of secondary metabolites from endophytic fungi received great attention in the 1990s and 2000s, when the controversy around taxol production from Taxus spp. endophytes was at its height. Since then, hundreds of reports have described the isolation and characterization of putative secondary metabolites from endophytic fungi. However, only very few studies also report the genetic basis for these phenotypic observations. With low sequencing cost and fast sample turnaround, genetics- and genomics-based approaches have risen to become comprehensive approaches to study natural products from a wide-range of organisms, especially to elucidate underlying biosynthetic pathways. However, in the field of fungal endophyte biology, elucidation of biosynthetic pathways is still a major challenge. As a relatively poorly investigated group of microorganisms, even in the light of recent efforts to sequence more fungal genomes, such as the 1000 Fungal Genomes Project at the Joint Genome Institute (JGI), the basis for bioprospecting of enzymes and pathways from endophytic fungi is still rather slim. In this review we want to discuss the current approaches and tools used to associate phenotype and genotype to elucidate biosynthetic pathways of secondary metabolites in endophytic fungi through the lens of bioprospecting. This review will point out the reported successes and shortcomings, and discuss future directions in sampling, and genetics and genomics of endophytic fungi. Identifying responsible biosynthetic genes for the numerous secondary metabolites isolated from endophytic fungi opens the opportunity to explore the genetic potential of producer strains to discover novel secondary metabolites and enhance secondary metabolite production by metabolic engineering resulting in novel and more affordable medicines and food additives.