Post-genomic Era Research Articles

Genebanking plant genetic resources in the postgenomic era

Genebanking, the process of preserving genetic resources, is a central practice in the modern management of crop genetics, especially for the species used for food and agriculture. Closely interrelated networks of local, national and global actors are responsible for ex situ conservation. They all seek to make plant genetic resources accessible for all and now face new challenges arising from digitisation. Plant sciences are entering the postgenomic era, moving fast from initially providing a single reference genome for each species (genomics), to harnessing the extent of diversity within crop species (pangenomics) and among their relatives (referred to as postgenomics). This paper describes the extent to which ex situ collections have already undergone a digital shift, or are planning to do so, and the potential impact of this postgenomic-induced dematerialisation on the global governance of plant genetic resources. In turn, digitising material (seed) collection changes the relationship between genebanks and genomic databases. Comprehensive genomic characterisation of genebank accessions is ongoing, and I argue here that these efforts may provide a unique opportunity for genebanks to further embrace the moral, ethical and ultimately political principles on which they were built. Repurposing genebanks as decentralised digital biocentres could help relocate capabilities and stewardship over genetic resources. Empowering local farmers by providing access, promoting the use and unlocking benefits from state-of-the-art tools of modern plant breeding may allow bridging the breeding divide. However, to accomplish such a paradigm shift, genebanks require a strong political mandate that must primarily originate from the access and benefit-sharing framework. Only so may the global challenges associated with the loss of biodiversity and food insecurity be addressed.

Harnessing the Power of Electronic Health Records and Genomics for Drug Discovery.

A long-standing recognition that information from human genetics studies has the potential to accelerate drug discovery has led to decades of research on how to leverage genetic and phenotypic information for drug discovery. Established simple and advanced statistical methods that allow the simultaneous analysis of genotype and clinical phenotype data by genome- and phenome-wide analyses, colocalization analyses with quantitative trait loci data from transcriptomics and proteomics data sets from different tissues, and Mendelian randomization are essential tools for drug development in the postgenomic era. Numerous studies have demonstrated how genomic data provide opportunities for the identification of new drug targets, the repurposing of drugs, and drug safety analyses. With an increase in the number of biobanks that enable linking in-depth omics data with rich repositories of phenotypic traits via electronic health records, more powerful ways for the evaluation and validation of drug targets will continue to expand across different disciplines of clinical research.

A prolific and robust whole-genome genotyping method using PCR amplification via primer-template mismatched annealing.

Whole-genome genotyping methods are important for breeding. However, it has been challenging to develop a robust method for simultaneous foreground and background genotyping that can easily be adapted to different genes and species. In our study, we accidently discovered that in adapter ligation-mediated PCR, the amplification by primer-template mismatched annealing (PTMA) along the genome could generate thousands of stable PCR products. Based on this observation, we consequently developed a novel method for simultaneous foreground and background integrated genotyping by sequencing (FBI-seq) using one specific primer, in which foreground genotyping is performed by primer-template perfect annealing (PTPA), while background genotyping employs PTMA. Unlike DNA arrays, multiple PCR, or genome target enrichments, FBI-seq requires little preliminary work for primer design and synthesis, and it is easily adaptable to different foreground genes and species. FBI-seq therefore provides a prolific, robust, and accurate method for simultaneous foreground and background genotyping to facilitate breeding in the post-genomics era.

URA3 as a Selectable Marker for Disruption and Functional Assessment of PacC Gene in the Entomopathogenic Fungus Isaria javanica.

An effective selection marker is necessary for genetic engineering and functional genomics research in the post-genomic era. Isaria javanica is an important entomopathogenic fungus with a broad host range and prospective biocontrol potentials. Given that no antibiotic marker is available currently in this fungus, developing an effective selection marker is necessary. In this study, by applying overlap PCR and split-marker deletion strategy, combining PEG-mediated protoplasm transformation method, the uridine auxotrophy gene (ura3) in the I. javanica genome was knocked out. Then, using this transformation system, the pH response transcription factor gene (IjpacC) was disrupted successfully. Loss of IjpacC gene results in an obvious decrease in conidial production, but little impact on mycelial growth. The virulence of the ΔIjpacC mutant on caterpillars is similar to that of the wild-type strain. RT-qPCR detection shows that expression level of an acidic-expressed S53 gene (IF1G_06234) in ΔIjpacC mutant is more significantly upregulated than in the wild-type strain during the fungal infection on caterpillars. Our results indicate that a markerless transformation system based upon complementation of uridine auxotrophy is successfully developed in I. javanica, which is useful for exploring gene function and for genetic engineering to enhance biological control potential of the fungus.

Gay Genes in the Postgenomic Era

This roundtable analyzes the first genome-wide association study (GWAS) that sought to identify the genetic variations that correlate with same-sex sexual behavior. Drawing on over 450,000 individuals’ genetic material from the UK Biobank and 23andMe, the 2019 study concluded that “many loci with individually small effects,” which are spread across the entire genome, contribute in statistically significant but highly unreliable ways to an individual's sexual behavior. The study was thus greeted by geneticists, science journalists, and even some LGBTQ+ advocates as heralding the demise of the mythical “gay gene.” However, the study itself did not drive a stake through the heart of the “born this way” idea. In fact, the researchers framed their efforts as having revealed the “genetic architecture”—which is to say the blueprint or design—of same-sex sexual behavior. Stephanie Clare, Patrick R. Grzanka, and Joanna Wuest argue that the 2019 GWAS marks a moment of both flux and continuity: a recognition of sexuality's complexity and contingency alongside a continued affective, ideological, and economic investment in biology's role in telling fundamental truths about behavior and identity. The study's recognition of the complexity of sexuality should not be mistaken as some wish fulfillment of queer theory; rather, the dream of bioessentialism, entangled with its continued production of inequality, is still alive in the postgenomic era.

A Robust Methodology for Assessing Homoeolog-Specific Expression.

Angiosperm evolution is marked by numerous, recurring polyploidization events. While hybridization and polyploidization have greatly increased the degree of genetic and phenotypic diversity in plants, the mechanisms underlying changes in the genotype-to-phenotype relationships remain unclear. As the field of natural sciences continues to expand during the post-genomic era, large datasets are becoming increasingly common. However, the development of tools and workflows available to robustly assess these changes have lagged behind data production. A robust homoeolog-specific expression analysis strongly depends upon proper homoeolog calling, the ability to account for reference sequence biases, flexible and accurate methods for dealing with residual bias, and a reproducible workflow. To that end, this chapter aims to provide a detailed description of the potential pitfalls encountered while estimating homoeolog-specific expression as well as provide a workflow that allows for robust inferences based on precise estimates of expression changes.

G-SAIP: Graphical Sequence Alignment Through Parallel Programming in the Post-Genomic Era.

A common task in bioinformatics is to compare DNA sequences to identify similarities between organisms at the sequence level. An approach to such comparison is the dot-plots, a 2-dimensional graphical representation to analyze DNA or protein alignments. Dot-plots alignment software existed before the sequencing revolution, and now there is an ongoing limitation when dealing with large-size sequences, resulting in very long execution times. High-Performance Computing (HPC) techniques have been successfully used in many applications to reduce computing times, but so far, very few applications for graphical sequence alignment using HPC have been reported. Here, we present G-SAIP (Graphical Sequence Alignment in Parallel), a software capable of spawning multiple distributed processes on CPUs, over a supercomputing infrastructure to speed up the execution time for dot-plot generation up to 1.68× compared with other current fastest tools, improve the efficiency for comparative structural genomic analysis, phylogenetics because the benefits of pairwise alignments for comparison between genomes, repetitive structure identification, and assembly quality checking.

Advances in the field of phage-based therapy with special emphasis on computational resources.

In the current era, one of the major challenges is to manage the treatment of drug/antibiotic-resistant strains of bacteria. Phage therapy, a century-old technique, may serve as an alternative to antibiotics in treating bacterial infections caused by drug-resistant strains of bacteria. In this review, a systematic attempt has been made to summarize phage-based therapy in depth. This review has been divided into the following two sections: general information and computer-aided phage therapy (CAPT). In the case of general information, we cover the history of phage therapy, the mechanism of action, the status of phage-based products (approved and clinical trials) and the challenges. This review emphasizes CAPT, where we have covered primary phage-associated resources, phage prediction methods and pipelines. This review covers a wide range of databases and resources, including viral genomes and proteins, phage receptors, host genomes of phages, phage-host interactions and lytic proteins. In the post-genomic era, identifying the most suitable phage for lysing a drug-resistant strain of bacterium is crucial for developing alternate treatments for drug-resistant bacteria and this remains a challenging problem. Thus, we compile all phage-associated prediction methods that include the prediction of phages for a bacterial strain, the host for a phage and the identification of interacting phage-host pairs. Most of these methods have been developed using machine learning and deep learning techniques. This review also discussed recent advances in the field of CAPT, where we briefly describe computational tools available for predicting phage virions, the life cycle of phages and prophage identification. Finally, we describe phage-based therapy's advantages, challenges and opportunities.

Inference about quantitative traits under selection: a Bayesian revisitation for the post-genomic era

BackgroundSelection schemes distort inference when estimating differences between treatments or genetic associations between traits, and may degrade prediction of outcomes, e.g., the expected performance of the progeny of an individual with a certain genotype. If input and output measurements are not collected on random samples, inferences and predictions must be biased to some degree. Our paper revisits inference in quantitative genetics when using samples stemming from some selection process. The approach used integrates the classical notion of fitness with that of missing data. Treatment is fully Bayesian, with inference and prediction dealt with, in an unified manner. While focus is on animal and plant breeding, concepts apply to natural selection as well. Examples based on real data and stylized models illustrate how selection can be accounted for in four different situations, and sometimes without success.ResultsOur flexible “soft selection” setting helps to diagnose the extent to which selection can be ignored. The clear connection between probability of missingness and the concept of fitness in stylized selection scenarios is highlighted. It is not realistic to assume that a fixed selection threshold t holds in conceptual replication, as the chance of selection depends on observed and unobserved data, and on unequal amounts of information over individuals, aspects that a “soft” selection representation addresses explicitly. There does not seem to be a general prescription to accommodate potential distortions due to selection. In structures that combine cross-sectional, longitudinal and multi-trait data such as in animal breeding, balance is the exception rather than the rule. The Bayesian approach provides an integrated answer to inference, prediction and model choice under selection that goes beyond the likelihood-based approach, where breeding values are inferred indirectly.ConclusionsThe approach used here for inference and prediction under selection may or may not yield the best possible answers. One may believe that selection has been accounted for diligently, but the central problem of whether statistical inferences are good or bad does not have an unambiguous solution. On the other hand, the quality of predictions can be gauged empirically via appropriate training-testing of competing methods.

IMAPping the Perturb-Atlas

Abstract A key objective of the research in the postgenomic era is to decipher the functions of the mammalian genome, which has remained largely enigmatic despite intensive efforts in the functional genomics field over the past two decades. To attack this problem, we have combined the CRISPR-Cas and Cre-Lox technologies to develop iMAP (inducible Mosaic Animal for Perturbation), a transformative tool for rapidly unraveling mammalian genome function in situ. Furthermore, we have used iMAP to rapidly construct a “Perturb-Atlas” profiling the functions of 90 protein-coding genes across 39 tissues in mice, which has offered rich insights into gene functions difficult to readily obtain using conventional mouse gene-targeting models. In this research highlight, we offer a brief primer on the iMAP technology, outlining its mechanism, strengths and limitations, and pointing out future directions of research in the area.

The progress and challenges of circRNA for diabetic foot ulcers: A mini-review.

Since the Human Genome Project was successfully completed, humanity has entered a post-genome era, and the second-generation sequencing technology has gradually progressed and become more accurate. Meanwhile, circRNAs plays a crucial role in the regulation of diseases and potential clinical applications has gradually attracted the attention of physicians. However, the mechanisms of circRNAs regulation at the cellular and molecular level of diabetic foot ulcer (DFU) is still not well-understood. With the deepening of research, there have been many recent studies conducted to explore the effect of circRNAs on DFU. In this mini-review, we discuss the potential role of circRNAs as therapeutic targets and diagnostic markers for DFU in order to gain a better understanding of the molecular mechanisms that underlie the development of DFU and to establish a theoretical basis for accurate treatment and effective prevention.

Dynamic Interactomics by Cross-Linking Mass Spectrometry: Mapping the Daily Cell Life in Postgenomic Era.

The majority of processes that occur in daily cell life are modulated by hundreds to thousands of dynamic protein-protein interactions (PPI). The resulting protein complexes constitute a tangled network that, with its continuous remodeling, builds up highly organized functional units. Thus, defining the dynamic interactome of one or more proteins allows determining the full range of biological activities these proteins are capable of. This conceptual approach is poised to gain further traction and significance in the current postgenomic era wherein the treatment of severe diseases needs to be tackled at both genomic and PPI levels. This also holds true for COVID-19, a multisystemic disease affecting biological networks across the biological hierarchy from genome to proteome to metabolome. In this overarching context and the current historical moment of the COVID-19 pandemic where systems biology increasingly comes to the fore, cross-linking mass spectrometry (XL-MS) has become highly relevant, emerging as a powerful tool for PPI discovery and characterization. This expert review highlights the advanced XL-MS approaches that provide in vivo insights into the three-dimensional protein complexes, overcoming the static nature of common interactomics data and embracing the dynamics of the cell proteome landscape. Many XL-MS applications based on the use of diverse cross-linkers, MS detection methods, and predictive bioinformatic tools for single proteins or proteome-wide interactions were shown. We conclude with a future outlook on XL-MS applications in the field of structural proteomics and ways to sustain the remarkable flexibility of XL-MS for dynamic interactomics and structural studies in systems biology and planetary health.

Capturing conservation in the post-genomics era: a book review of “Conservation and the Genomics of Populations.”

Capturing conservation in the post-genomics era: a book review of “Conservation and the Genomics of Populations.”

Making "Sense" of Ecology from a Genetic Perspective: Caenorhabditis elegans, Microbes and Behavior.

Our knowledge of animal and behavior in the natural ecology is based on over a century's worth of valuable field studies. In this post-genome era, however, we recognize that genes are the underpinning of ecological interactions between two organisms. Understanding how genes contribute to animal ecology, which is essentially the intersection of two genomes, is a tremendous challenge. The bacterivorous nematode Caenorhabditis elegans, one of the most well-known genetic animal model experimental systems, experiences a complex microbial world in its natural habitat, providing us with a window into the interplay of genes and molecules that result in an animal-microbial ecology. In this review, we will discuss C. elegans natural ecology, how the worm uses its sensory system to detect the microbes and metabolites that it encounters, and then discuss some of the fascinating ecological dances, including behaviors, that have evolved between the nematode and the microbes in its environment.

Enhanced Rishirilide Biosynthesis by a Rare In-Cluster Phosphopantetheinyl Transferase in Streptomyces xanthophaeus.

Phosphopantetheinyl transferases (PPTases) play important roles in activating apo-acyl carrier proteins (apo-ACPs) and apo-peptidyl carrier proteins (apo-PCPs) in both primary and secondary metabolism. PPTases catalyze the posttranslational modifications of those carrier proteins by covalent attachment of the 4'-phosphopantetheine group to a conserved serine residue. The protein-protein interactions between a PPTase and a cognate acyl or peptidyl carrier protein have important regulatory functions in microbial biosynthesis, but the molecular mechanism underlying their specific recognition remains elusive. In this study, we identified a new rishirilide biosynthetic gene cluster with a rare in-cluster PPTase from Streptomyces xanthophaeus no2. The function of this Sfp-type PPTase, SxrX, in rishirilide production was confirmed using genetic mutagenesis and biochemical characterization. We applied molecular modeling and site-directed mutagenesis to identify key residues mediating the protein-protein interaction between SxrX and its cognate ACP. In addition, six natural products were isolated from wild-type S. xanthophaeus no2 and the ΔsxrX mutant, including rishirilide A and lupinacidin A, that exhibited antimicrobial and anticancer activities, respectively. SxrX is the first Sfp-type PPTase identified from an aromatic polyketide biosynthetic gene cluster and shown to be responsible for high-level production of rishirilide derivatives. IMPORTANCE Genome mining has been a vital means for natural product drug discovery in the postgenomic era. The rishirilide-type polyketides have attracted attention due to their potent bioactivity, but the poor robustness of production hosts has limited further research and development. This study not only identifies a hyperproducer of rishirilides but also reveals a rare, in-cluster PPTase SxrX that plays an important role in boosting rishirilide biosynthesis. Experimental and computational investigations revealed new insights on the protein-protein interaction between SxrX and its cognate ACP with wide implications for understanding polyketide biosynthesis.

Spontaneous, Artificial, and Genome Editing-Mediated Mutations in Prunus.

Mutation is a source of genetic diversity widely used in breeding programs for the acquisition of agronomically interesting characters in commercial varieties of the Prunus species, as well as in the rest of crop species. Mutation can occur in nature at a very low frequency or can be induced artificially. Spontaneous or bud sport mutations in somatic cells can be vegetatively propagated to get an individual with the mutant phenotype. Unlike animals, plants have unlimited growth and totipotent cells that let somatic mutations to be transmitted to the progeny. On the other hand, in vitro tissue culture makes it possible to induce mutation in plant material and perform large screenings for mutant's selection and cleaning of chimeras. Finally, targeted mutagenesis has been boosted by the application of CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)/Cas9 and Transcription activator-like effector nuclease (TALEN) editing technologies. Over the last few decades, environmental stressors such as global warming have been threatening the supply of global demand for food based on population growth in the near future. For this purpose, the release of new varieties adapted to such changes is a requisite, and selected or generated Prunus mutants by properly regulated mechanisms could be helpful to this task. In this work, we reviewed the most relevant mutations for breeding traits in Prunus species such as flowering time, self-compatibility, fruit quality, and disease tolerance, including new molecular perspectives in the present postgenomic era including CRISPR/Cas9 and TALEN editing technologies.

Exploring prostate cancer in the post-genomic era

In the Special Issue on Prostate Cancer, we have invited 25 researchers or clinicians from prostate cancer community to review the cutting-edge topics in this field. In particular, the mini-reviews have covered various basic science and clinical aspects in prostate cancer, including prostate epithelial stem cells or progenitors, androgen and androgen receptor pathways, tumor modeling, genomics, different cell-autonomous and non-cell-autonomous mechanisms as well as various clinical issues encompassing diagnosis, risk stratification and treatments.

Preface for special issue on multi-omics frontier technologies

With wide applications of genomics, transcriptomics, proteomics and metabolomics in the post-genome era, functional explanation has become the central task in life science research, and multi-omics data integrative analysis has become an indispensable strategy for uncovering the underlying biological mechanism. This special issue aimed to introduce the related research advances and applications in multi-omics by inviting the domestic experts. In total, 28 papers have been collected in this issue, for researcher's reference in multi-omics.

Systemic therapy for early-stage breast cancer: learning from the past to build the future.

The treatment of breast cancer has improved dramatically over the past century, from a strictly surgical approach to a coordinated one, including local and systemic therapies. Systemic therapies for early-stage disease were initially tested against observation or placebo only in adjuvant trials. Subsequent clinical trials focusing on treatment ‘fine-tuning’ had a marked increase in cohort size, duration and costs, leading to a growing interest in the neoadjuvant setting in the past decade. Neoadjuvant trial designs have the advantages of enabling the direct evaluation of treatment effects on tumour diameter and offer unique translational research opportunities through the comparative analysis of tumour biology before, during and after treatment. Current technologies enabling the identification of better predictive biomarkers are shaping the new era of (neo)adjuvant trials. An urgent need exists to reinforce collaboration between the pharmaceutical industry and academia to share data and thus establish large databases of biomarker data coupled with patient outcomes that are easily accessible to the scientific community. In this Review, we summarize the evolution of (neo)adjuvant trials from the pre-genomic to the post-genomic era and provide critical insights into how neoadjuvant studies are currently designed, discussing the need for better end points and treatment strategies that are more personalized, including in the post-neoadjuvant setting.

DPProm: A Two-Layer Predictor for Identifying Promoters and Their Types on Phage Genome Using Deep Learning.

With the number of phage genomes increasing, it is urgent to develop new bioinformatics methods for phage genome annotation. Promoter, a DNA region, is important for gene transcriptional regulation. In the era of post-genomics, the availability of data makes it possible to establish computational models for promoter identification with robustness. In this work, we introduce DPProm, a two-layer model composed of DPProm-1L and DPProm-2L, to predict promoters and their types for phages. On the first layer, as a dual-channel deep neural network ensemble method fusing multi-view features (sequence feature and handcrafted feature), the model DPProm-1L is proposed to identify whether a DNA sequence is a promoter or non-promoter. The sequence feature is extracted with convolutional neural network (CNN). And the handcrafted feature is the combination of free energy, GC content, cumulative skew, and Z curve features. On the second layer, DPProm-2L based on CNN is trained to predict the promoters' types (host or phage). For the realization of prediction on the whole genomes, the model DPProm, combines with a novel sequence data processing workflow, which contains sliding window and merging sequences modules. Experimental results show that DPProm outperforms the state-of-the-art methods, and decreases the false positive rate effectively on whole genome prediction. Furthermore, we provide a user-friendly web at http://bioinfo.ahu.edu.cn/DPProm. We expect that DPProm can serve as a useful tool for identification of promoters and their types.