The 2022 Nucleic Acids Research database issue and the online molecular biology database collection

Efficient genome-editing tools to engineer the recalcitrant non-model industrial microorganism Zymomonas mobilis

Current biotechnology relies on a few well-studied model organisms, such as Escherichia coli and Saccharomyces cerevisiae, for which abundant information and efficient toolkits are available for genetic manipulation, but which lack industrially favorable characteristics. Non-model industrial microorganisms usually do not have effective and/or efficient genome-engineering toolkits, which hampers the development of microbial cell factories to meet the fast-growing bioeconomy. In this study, using the non-model ethanologenic bacterium Zymomonas mobilis as an example, we developed a workflow to mine and temper the elements of restriction-modification (R-M), CRISPR/Cas, toxin-antitoxin (T-A) systems, and native plasmids, which are hidden within industrial microorganisms themselves, as efficient genome-editing toolkits, and established a genome-wide iterative and continuous editing (GW-ICE) system for continuous genome editing with high efficiency. This research not only provides tools and pipelines for engineering the non-model polyploid industrial microorganism Z. mobilis efficiently, but also sets a paradigm to overcome biotechnological limitations in other genetically recalcitrant non-model industrial microorganisms.

A computational and machine learning approach to identify GPR40-targeting agonists for neurodegenerative disease treatment

The G protein-coupled receptor 40 (GPR40) is known to exert a significant influence on neurogenesis and neurodevelopment within the central nervous system of both humans and rodents. Research findings indicate that the activation of GPR40 by an agonist has been observed to promote the proliferation and viability of hypothalamus cells in the human body. The objective of the present study is to discover new agonist compounds for the GPR40 protein through the utilization of machine learning and pharmacophore-based screening techniques, in conjunction with other computational methodologies such as docking, molecular dynamics simulations, free energy calculations, and investigations of the free energy landscape. In the course of our investigation, we successfully identified five unreported agonist compounds that exhibit robust docking score, displayed stability in ligand RMSD and consistent hydrogen bonding with the receptor in the MD trajectories. Free energy calculations were observed to be higher than control molecule. The measured binding affinities of compounds namely 1, 3, 4, 6 and 10 were -13.9, -13.5, -13.4, -12.9, and -12.1 Kcal/mol, respectively. The identified molecular agonist that has been found can be assessed in terms of its therapeutic efficacy in the treatment of neurological diseases.

Targeting cancer with small-molecule pan-KRAS degraders

Mutations in the Kirsten rat sarcoma viral oncogene homolog (KRAS) protein are highly prevalent in cancer. However, small-molecule concepts that address oncogenic KRAS alleles remain elusive beyond replacing glycine at position 12 with cysteine (G12C), which is clinically drugged through covalent inhibitors. Guided by biophysical and structural studies of ternary complexes, we designed a heterobifunctional small molecule that potently degrades 13 out of 17 of the most prevalent oncogenic KRAS alleles. Compared with inhibition, KRAS degradation results in more profound and sustained pathway modulation across a broad range of KRAS mutant cell lines, killing cancer cells while sparing models without genetic KRAS aberrations. Pharmacological degradation of oncogenic KRAS was tolerated and led to tumor regression in vivo. Together, these findings unveil a new path toward addressing KRAS-driven cancers with small-molecule degraders.

Multi-omic analysis tools for microbial metabolites prediction

How to resolve the metabolic dark matter of microorganisms has long been a challenging problem in discovering active molecules. Diverse omics tools have been developed to guide the discovery and characterization of various microbial metabolites, which make it gradually possible to predict the overall metabolites for individual strains. The combinations of multi-omic analysis tools effectively compensates for the shortcomings of current studies that focus only on single omics or a broad class of metabolites. In this review, we systematically update, categorize and sort out different analysis tools for microbial metabolites prediction in the last five years to appeal for the multi-omic combination on the understanding of the metabolic nature of microbes. First, we provide the general survey on different updated prediction databases, webservers, or software that based on genomics, transcriptomics, proteomics, and metabolomics, respectively. Then, we discuss the essentiality on the integration of multi-omics data to predict metabolites of different microbial strains and communities, as well as stressing the combination of other techniques, such as systems biology methods and data-driven algorithms. Finally, we identify key challenges and trends in developing multi-omic analysis tools for more comprehensive prediction on diverse microbial metabolites that contribute to human health and disease treatment.

Beyond blast: enabling microbiologists to better extract literature, taxonomic distributions and gene neighbourhood information for protein families

Capturing the published corpus of information on all members of a given protein family should be an essential step in any study focusing on specific members of that family. Using a previously gathered dataset of more than 280 references mentioning a member of the DUF34 (NIF3/Ngg1-interacting Factor 3) family, we evaluated the efficiency of different databases and search tools, and devised a workflow that experimentalists can use to capture the most information published on members of a protein family in the least amount of time. To complement this workflow, web-based platforms allowing for the exploration of protein family members across sequenced genomes or for the analysis of gene neighbourhood information were reviewed for their versatility and ease of use. Recommendations that can be used for experimentalist users, as well as educators, are provided and integrated within a customized, publicly accessible Wiki.

Beyond Blast: Enabling Microbiologists to Better Extract Literature, Taxonomic Distributions and Gene Neighborhood Information for Protein Families

Capturing the published corpus of information on all members of a given protein family should be an essential step in any study focusing on specific members of that said family. Using a previously gathered dataset of more than 280 references mentioning a member of the DUF34 (NIF3/Ngg1-interacting Factor 3), we evaluated the efficiency of different databases and search tools, and devised a workflow that experimentalists can use to capture the most published information on members of a protein family in the least amount of time. To complement this workflow, web-based platforms allowing for the exploration of protein family members across sequenced genomes or for the analysis of gene neighborhood information were reviewed for their versatility and ease of use. Recommendations that can be used for experimentalist users, as well as educators, are provided and integrated within a customized, publicly accessible Wiki.

Statistical integration of multi-omics and drug screening data from cell lines

Data integration methods are used to obtain a unified summary of multiple datasets. For multi-modal data, we propose a computational workflow to jointly analyze datasets from cell lines. The workflow comprises a novel probabilistic data integration method, named POPLS-DA, for multi-omics data. The workflow is motivated by a study on synucleinopathies where transcriptomics, proteomics, and drug screening data are measured in affected LUHMES cell lines and controls. The aim is to highlight potentially druggable pathways and genes involved in synucleinopathies. First, POPLS-DA is used to prioritize genes and proteins that best distinguish cases and controls. For these genes, an integrated interaction network is constructed where the drug screen data is incorporated to highlight druggable genes and pathways in the network. Finally, functional enrichment analyses are performed to identify clusters of synaptic and lysosome-related genes and proteins targeted by the protective drugs. POPLS-DA is compared to other single- and multi-omics approaches. We found that HSPA5, a member of the heat shock protein 70 family, was one of the most targeted genes by the validated drugs, in particular by AT1-blockers. HSPA5 and AT1-blockers have been previously linked to α-synuclein pathology and Parkinson's disease, showing the relevance of our findings. Our computational workflow identified new directions for therapeutic targets for synucleinopathies. POPLS-DA provided a larger interpretable gene set than other single- and multi-omic approaches. An implementation based on R and markdown is freely available online.

Ten simple rules for managing laboratory information

Information is the cornerstone of research, from experimental (meta)data and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging laboratory information management systems to transform this large information load into useful scientific findings.

Statistical learning quantifies transposable element-mediated cis-regulation

BACKGROUND

Transposable elements (TEs) have colonized the genomes of most metazoans, and many TE-embedded sequences function as cis-regulatory elements (CREs) for genes involved in a wide range of biological processes from early embryogenesis to innate immune responses. Because of their repetitive nature, TEs have the potential to form CRE platforms enabling the coordinated and genome-wide regulation of protein-coding genes by only a handful of trans-acting transcription factors (TFs).

RESULTS

Here, we directly test this hypothesis through mathematical modeling and demonstrate that differences in expression at protein-coding genes alone are sufficient to estimate the magnitude and significance of TE-contributed cis-regulatory activities, even in contexts where TE-derived transcription fails to do so. We leverage hundreds of overexpression experiments and estimate that, overall, gene expression is influenced by TE-embedded CREs situated within approximately 500 kb of promoters. Focusing on the cis-regulatory potential of TEs within the gene regulatory network of human embryonic stem cells, we find that pluripotency-specific and evolutionarily young TE subfamilies can be reactivated by TFs involved in post-implantation embryogenesis. Finally, we show that TE subfamilies can be split into truly regulatorily active versus inactive fractions based on additional information such as matched epigenomic data, observing that TF binding may better predict TE cis-regulatory activity than differences in histone marks.

CONCLUSION

Our results suggest that TE-embedded CREs contribute to gene regulation during and beyond gastrulation. On a methodological level, we provide a statistical tool that infers TE-dependent cis-regulation from RNA-seq data alone, thus facilitating the study of TEs in the next-generation sequencing era.

The consequences of data dispersion in genomics: a comparative analysis of data sources for precision medicine

BACKGROUND

Genomics-based clinical diagnosis has emerged as a novel medical approach to improve diagnosis and treatment. However, advances in sequencing techniques have increased the generation of genomics data dramatically. This has led to several data management problems, one of which is data dispersion (i.e., genomics data is scattered across hundreds of data repositories). In this context, geneticists try to remediate the above-mentioned problem by limiting the scope of their work to a single data source they know and trust. This work has studied the consequences of focusing on a single data source rather than considering the many different existing genomics data sources.

METHODS

The analysis is based on the data associated with two groups of disorders (i.e., oncology and cardiology) accessible from six well-known genomic data sources (i.e., ClinVar, Ensembl, GWAS Catalog, LOVD, CIViC, and CardioDB). Two dimensions have been considered in this analysis, namely, completeness and concordance. Completeness has been evaluated at two levels. First, by analyzing the information provided by each data source with regard to a conceptual schema data model (i.e., the schema level). Second, by analyzing the DNA variations provided by each data source as related to any of the disorders selected (i.e., the data level). Concordance has been evaluated by comparing the consensus among the data sources regarding the clinical relevance of each variation and disorder.

RESULTS

The data sources with the highest completeness at the schema level are ClinVar, Ensembl, and CIViC. ClinVar has the highest completeness at the data level data source for the oncology and cardiology disorders. However, there are clinically relevant variations that are exclusive to other data sources, and they must be considered in order to provide the best clinical diagnosis. Although the information available in the data sources is predominantly concordant, discordance among the analyzed data exist. This can lead to inaccurate diagnoses.

CONCLUSION

Precision medicine analyses using a single genomics data source leads to incomplete results. Also, there are concordance problems that threaten the correctness of the genomics-based diagnosis results.

iORbase: A database for the prediction of the structures and functions of insect olfactory receptors

Insect olfactory receptors (iORs) with atypical 7-transmembrane domains, unlike Chordata olfactory receptors, are not in the GPCR protein family. iORs selectively bind to volatile ligands in the environment and affect essential insect behaviors. In this study, we constructed a new platform (iORbase, https://www.iorbase.com) for the structural and functional analysis of iORs based on a combined algorithm for gene annotation and protein structure prediction. Moreover, it provides the option to calculate the binding affinities and binding residues between iORs and pheromone molecules by virtual screening of docking. Furthermore, iORbase supports the automatic structural and functional prediction of user-submitted iORs or pheromones. iORbase contains the well-analyzed results of approximately 6 000 iORs and their 3D protein structures identified from 59 insect species and 2 077 insect pheromones from the literature, as well as approximately 12 million pairs of simulated interactions between functional iORs and pheromones. We also built 4 online modules, iORPDB, iInteraction, iModelTM, and iOdorTool to easily retrieve and visualize the 3D structures and interactions. iORbase can help greatly improve the experimental efficiency and success rate, identify new insecticide targets, or develop electronic nose technology. This study will shed light on the olfactory recognition mechanism and evolutionary characteristics from the perspectives of omics and macroevolution.

Immune Activation and Inflammatory Response Mediated by the NOD/Toll-like Receptor Signaling Pathway-The Potential Mechanism of Bullfrog (Lithobates catesbeiana) Meningitis Caused by Elizabethkingia miricola

Elizabethkingia miricola is an emerging opportunistic pathogen that is highly pathogenic in both immunocompromised humans and animals. Once the disease occurs, treatment can be very difficult. Therefore, a deep understanding of the pathological mechanism of Elizabethkingia miricola is the key to the prevention and control of the disease. In this study, we isolated the pathogenic bacteria from bullfrogs with dark skin color, weak limbs, wryneck, and cataracts. Via subsequent morphological observations and a 16S rRNA gene sequence analysis, the pathogen was identified as Elizabethkingia miricola. The histopathological and transmission electron microscopy analysis revealed that the brain was the main target organ. Therefore, brain samples from diseased and healthy bullfrogs were used for the RNA-Seq analysis. The comparative transcriptome analysis revealed that the diseased bullfrog brain was characterized by the immune activation and inflammatory response, which were mediated by the "NOD-like receptor signaling pathway" and the "Toll-like receptor signaling pathway". We also performed qRT-PCR to examine the expression profile of inflammation-related genes, which further verified the reliability of our transcriptome data. Based on the above results, it was concluded that the NOD/Toll-like receptor-related networks that dominate the immune activation and inflammatory response were activated in the brain of Elizabethkingia miricola-infected bullfrogs. This study contributes to the search for therapeutic targets for bullfrog meningitis and provides basic information for establishing effective measures to prevent and control bullfrog meningitis.

Integration of Meta-Multi-Omics Data Using Probabilistic Graphs and External Knowledge

Multi-omics has the promise to provide a detailed molecular picture of biological systems. Although obtaining multi-omics data is relatively easy, methods that analyze such data have been lagging. In this paper, we present an algorithm that uses probabilistic graph representations and external knowledge to perform optimal structure learning and deduce a multifarious interaction network for multi-omics data from a bacterial community. Kefir grain, a microbial community that ferments milk and creates kefir, represents a self-renewing, stable, natural microbial community. Kefir has been shown to have a wide range of health benefits. We obtained a controlled bacterial community using the two most abundant and well-studied species in kefir grains: Lentilactobacillus kefiri and Lactobacillus kefiranofaciens. We applied growth temperatures of 30 °C and 37 °C and obtained transcriptomic, metabolomic, and proteomic data for the same 20 samples (10 samples per temperature). We obtained a multi-omics interaction network, which generated insights that would not have been possible with single-omics analysis. We identified interactions among transcripts, proteins, and metabolites, suggesting active toxin/antitoxin systems. We also observed multifarious interactions that involved the shikimate pathway. These observations helped explain bacterial adaptation to different stress conditions, co-aggregation, and increased activation of L. kefiranofaciens at 37 °C.

Alteration of DNMT1/DNMT3A by eribulin elicits global DNA methylation changes with potential therapeutic implications for triple-negative breast cancer

Triple-negative breast cancer (TNBC) is an aggressive disease subtype with limited treatment options. Eribulin is a chemotherapeutic approved for the treatment of advanced breast cancer that has been shown to elicit epigenetic changes. We investigated the effect of eribulin treatment on genome-scale DNA methylation patterns in TNBC cells. Following repeated treatment, The results showed that eribulin-induced changes in DNA methylation patterns evident in persister cells. Eribulin also affected the binding of transcription factors to genomic ZEB1 binding sites and regulated several cellular pathways, including ERBB and VEGF signaling and cell adhesion. Eribulin also altered the expression of epigenetic modifiers including DNMT1, TET1, and DNMT3A/B in persister cells. Data from primary human TNBC tumors supported these findings: DNMT1 and DNMT3A levels were altered by eribulin treatment in human primary TNBC tumors. Our results suggest that eribulin modulates DNA methylation patterns in TNBC cells by altering the expression of epigenetic modifiers. These findings have clinical implications for using eribulin as a therapeutic agent.

Characterization of Malectin/Malectin-like Receptor-like Kinase Family Members in Foxtail Millet (Setaria italica L.)

Plant malectin/malectin-like receptor-like kinases (MRLKs) play crucial roles throughout the life course of plants. Here, we identified 23 SiMRLK genes from foxtail millet. All the SiMRLK genes were named according to the chromosomal distribution of the SiMRLKs in the foxtail millet genome and grouped into five subfamilies based on phylogenetic relationships and structural features. Synteny analysis indicated that gene duplication events may take part in the evolution of SiMRLK genes in foxtail millet. The expression profiles of 23 SiMRLK genes under abiotic stresses and hormonal applications were evaluated through qRT-PCR. The expression of SiMRLK1, SiMRLK3, SiMRLK7 and SiMRLK19 were significantly affected by drought, salt and cold stresses. Exogenous ABA, SA, GA and MeJA also obviously changed the transcription levels of SiMRLK1, SiMRLK3, SiMRLK7 and SiMRLK19. These results signified that the transcriptional patterns of SiMRLKs showed diversity and complexity in response to abiotic stresses and hormonal applications in foxtail millet.

Activation mechanism and activity of globupain, a thermostable C11 protease from the Arctic Mid-Ocean Ridge hydrothermal system

Deep-sea hydrothermal vent systems with prevailing extreme thermal conditions for life offer unique habitats to source heat tolearant enzymes with potential new enzymatic properties. Here, we present the novel C11 protease globupain , prospected from a metagenome-assembled genome of uncultivated Archaeoglobales sampled from the Soria Moria hydrothermal vent system located on the Arctic Mid- Ocean Ridges. By sequence comparisons against the MEROPS-MPRO database, globupain showed highest sequence identity to C11-like proteases present in human gut and intestinal bacteria,. Successful recombinant expression in Escherichia coli of the active zymogen and 13 mutant substitution variants allowed assesment of residues involved in maturation and activity of the enzyme. For activation, globupain required the addition of DTT and Ca²⁺. When activated, the 52 kDa proenzyme was processed at Lys ₁₃₇ and Lys ₁₄₄ into a 12 kDa light- and 32 kDa heavy chain heterodimer. A structurally conserved His ₁₃₂ /Cys ₁₈₅ catalytic dyad was responsible for the proteolytic activity, and the enzyme demonstrated the ability to activate in-trans . Globupain exhibited caseinolytic activity and showed a strong preference for arginine in the P1 position, with Boc-QAR- aminomethylcoumarin (AMC) as the best substrate out of a total of 17 fluorogenic AMC substrates tested. Globupain was thermostable (T _{m activated enzyme} = 94.51 ± 0.09°C) with optimal activity at 75 °C and pH 7.1. By characterizing globupain, our knowledge of the catalytic properties and activation mechanisms of temperature tolerant marine C11 proteases have been expanded. The unique combination of features such as elevated thermostability, activity at relatively low pH values, and ability to operate under high reducing conditions makes globupain a potential intriguing candidate for use in diverse industrial and biotechnology sectors.

Bioinformatics approaches and big data analytics opportunities in improving fisheries and aquaculture

Aquaculture has witnessed an excellent growth rate during the last two decades and offers huge potential to provide nutritional as well as livelihood security. Genomic research has contributed significantly toward the development of beneficial technologies for aquaculture. The existing high throughput technologies like next-generation technologies generate oceanic data which requires extensive analysis using appropriate tools. Bioinformatics is a rapidly evolving science that involves integrating gene based information and computational technology to produce new knowledge for the benefit of aquaculture. Bioinformatics provides new opportunities as well as challenges for information and data processing in new generation aquaculture. Rapid technical advancements have opened up a world of possibilities for using current genomics to improve aquaculture performance. Understanding the genes that govern economically relevant characteristics, necessitates a significant amount of additional research. The various dimensions of data sources includes next-generation DNA sequencing, protein sequencing, RNA sequencing gene expression profiles, metabolic pathways, molecular markers, and so on. Appropriate bioinformatics tools are developed to mine the biologically relevant and commercially useful results. The purpose of this scoping review is to present various arms of diverse bioinformatics tools with special emphasis on practical translation to the aquaculture industry.

A multiple-step screening protocol to identify norepinephrine and dopamine reuptake inhibitors for depression

Depression severely impairs the health of people all over the world. Cognitive dysfunction due to depression has resulted in a severe economic burden to family and society induced by the reduction of social functioning of patients. Norepinephrine-dopamine reuptake inhibitors (NDRIs) targeted with the human norepinephrine transporter (hNET) and distributed with the human dopamine transporter (hDAT) simultaneously treat depression and improve cognitive function, and they effectively prevent sexual dysfunction and other side effects. Because many patients continue to poorly respond to NDRIs, it is urgent to discover novel NDRI antidepressants that do not interfere with cognitive function. The aim of this work was to selectively identify novel NDRI candidates acting against hNET and hDAT from large compound libraries by a comprehensive strategy integrating support vector machine (SVM) models, ADMET, molecular docking, in vitro binding assays, molecular dynamics simulation, and binding energy calculation. First, 6522 compounds that do not inhibit the human serotonin transporter (hSERT) were obtained by SVM models of hNET, hDAT, and non-target hSERT with similarity analyses from compound libraries. ADMET and molecular docking were then used to identify compounds that could potently bind to the hNET and hDAT with satisfactory ADMET, and 4 compounds were successfully identified. According to their docking scores and ADMET information, 3719810 was advanced for profiling by in vitro assays as a novel NDRI lead compound due to its strongest druggability and balancing activities. Encouragingly, 3719810 performed comparative activities on two targets, with Ki values of 7.32 μM for hNET and 5.23 μM for hDAT. To obtain candidates with additional activities and balance the activities of 2 targets, 5 analogs were optimized, and 2 novel scaffold compounds were successively designed. By assessment of molecular docking, molecular dynamics simulations, and binding energy calculations, 5 compounds were validated as NDRI candidates with high activities, and 4 of them performed acceptable balancing activities acting on hNET and hDAT. This work supplied promising novel NDRIs for treatment of depression with cognitive dysfunction or other related neurodegenerative disorders, and also provided a strategy for highly efficient and cost-effective identification of inhibitors for dual targets with homologous non-targets.

WGS Data Collections: How Do Genomic Databases Transform Medicine?

As a scientific community we assumed that exome sequencing will elucidate the basis of most heritable diseases. However, it turned out it was not the case; therefore, attention has been increasingly focused on the non-coding sequences that encompass 98% of the genome and may play an important regulatory function. The first WGS-based datasets have already been released including underrepresented populations. Although many databases contain pooled data from several cohorts, recently the importance of local databases has been highlighted. Genomic databases are not only collecting data but may also contribute to better diagnostics and therapies. They may find applications in population studies, rare diseases, oncology, pharmacogenetics, and infectious and inflammatory diseases. Further data may be analysed with Al technologies and in the context of other omics data. To exemplify their utility, we put a highlight on the Polish genome database and its practical application.

Assembly and synthesis of the extracellular matrix in brown algae

In brown algae, the extracellular matrix (ECM) and its constitutive polymers play crucial roles in specialized functions, including algal growth and development. In this review we offer an integrative view of ECM construction in brown algae. We briefly report the chemical composition of its main constituents, and how these are interlinked in a structural model. We examine the ECM assembly at the tissue and cell level, with consideration on its structure in vivo and on the putative subcellular sites for the synthesis of its main constituents. We further discuss the biosynthetic pathways of two major polysaccharides, alginates and sulfated fucans, and the progress made beyond the candidate genes with the biochemical validation of encoded proteins. Key enzymes involved in the elongation of the glycan chains are still unknown and predictions have been made at the gene level. Here, we offer a re-examination of some glycosyltransferases and sulfotransferases from published genomes. Overall, our analysis suggests novel investigations to be performed at both the cellular and biochemical levels. First, to depict the location of polysaccharide structures in tissues. Secondly, to identify putative actors in the ECM synthesis to be functionally studied in the future.

RCSB Protein Data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), founding member of the Worldwide Protein Data Bank (wwPDB), is the US data center for the open-access PDB archive. As wwPDB-designated Archive Keeper, RCSB PDB is also responsible for PDB data security. Annually, RCSB PDB serves >10 000 depositors of three-dimensional (3D) biostructures working on all permanently inhabited continents. RCSB PDB delivers data from its research-focused RCSB.org web portal to many millions of PDB data consumers based in virtually every United Nations-recognized country, territory, etc. This Database Issue contribution describes upgrades to the research-focused RCSB.org web portal that created a one-stop-shop for open access to ∼200 000 experimentally-determined PDB structures of biological macromolecules alongside >1 000 000 incorporated Computed Structure Models (CSMs) predicted using artificial intelligence/machine learning methods. RCSB.org is a 'living data resource.' Every PDB structure and CSM is integrated weekly with related functional annotations from external biodata resources, providing up-to-date information for the entire corpus of 3D biostructure data freely available from RCSB.org with no usage limitations. Within RCSB.org, PDB structures and the CSMs are clearly identified as to their provenance and reliability. Both are fully searchable, and can be analyzed and visualized using the full complement of RCSB.org web portal capabilities.

Deciphering Macromolecular Interactions Involved in Abiotic Stress Signaling: A Review of Bioinformatics Analysis

Plant functioning and responses to abiotic stresses largely involve regulations at the transcriptomic level via complex interactions of signal molecules, signaling cascades, and regulators. Nevertheless, all the signaling networks involved in responses to abiotic stresses have not yet been fully established. The in-depth analysis of transcriptomes in stressed plants has become a relevant state-of-the-art methodology to study these regulations and signaling pathways that allow plants to cope with or attempt to survive abiotic stresses. The plant science and molecular biology community has developed databases about genes, proteins, protein-protein interactions, protein-DNA interactions and ontologies, which are valuable sources of knowledge for deciphering such regulatory and signaling networks. The use of these data and the development of bioinformatics tools help to make sense of transcriptomic data in specific contexts, such as that of abiotic stress signaling, using functional biological approaches. The aim of this chapter is to present and assess some of the essential online tools and resources that will allow novices in bioinformatics to decipher transcriptomic data in order to characterize the cellular processes and functions involved in abiotic stress responses and signaling. The analysis of case studies further describes how these tools can be used to conceive signaling networks on the basis of transcriptomic data. In these case studies, particular attention was paid to the characterization of abiotic stress responses and signaling related to chemical and xenobiotic stressors.

Ten quick tips for sequence-based prediction of protein properties using machine learning

The ubiquitous availability of genome sequencing data explains the popularity of machine learning-based methods for the prediction of protein properties from their amino acid sequences. Over the years, while revising our own work, reading submitted manuscripts as well as published papers, we have noticed several recurring issues, which make some reported findings hard to understand and replicate. We suspect this may be due to biologists being unfamiliar with machine learning methodology, or conversely, machine learning experts may miss some of the knowledge needed to correctly apply their methods to proteins. Here, we aim to bridge this gap for developers of such methods. The most striking issues are linked to a lack of clarity: how were annotations of interest obtained; which benchmark metrics were used; how are positives and negatives defined. Others relate to a lack of rigor: If you sneak in structural information, your method is not sequence-based; if you compare your own model to "state-of-the-art," take the best methods; if you want to conclude that some method is better than another, obtain a significance estimate to support this claim. These, and other issues, we will cover in detail. These points may have seemed obvious to the authors during writing; however, they are not always clear-cut to the readers. We also expect many of these tips to hold for other machine learning-based applications in biology. Therefore, many computational biologists who develop methods in this particular subject will benefit from a concise overview of what to avoid and what to do instead.

Considerations on the use of carrier screening testing in human reproduction: comparison between recommendations from the Italian Society of Human Genetics and other international societies

PURPOSE

Carrier screening (CS) is a term used to describe a genetic test performed on individuals without family history of genetic disorders, to investigate the carrier status for pathogenic variants associated with multiple recessive conditions. The advent of next-generation sequencing enabled simultaneous CS for an increasing number of conditions; however, a consensus on which diseases to include in gene panels and how to best develop the provision of CS is far to be reached. Therefore, the provision of CS is jeopardized and inconsistent and requires solving several important issues.

METHODS

In 2020, the Italian Society of Human Genetics (SIGU) established a working group composed of clinical and laboratory geneticists from public and private fields to elaborate a document to define indications and best practice of CS provision for couples planning a pregnancy.

RESULTS

Hereby, we present the outcome of the Italian working group's activity and compare it with previously published international recommendations (American College of Medical Genetics and Genomics (ACMG), American College of Obstetricians and Gynecologists (ACOG), and Royal Australian and New Zealand College of Obstetricians and Gynaecologists (RANZCOG)). We determine a core message on genetic counseling and nine main subject categories to explore, spanning from goals and execution to technical scientific, ethical, and socio-economic topics. Moreover, a level of agreement on the most critical points is discussed using a 5-point agreement scale, demonstrating a high level of consensus among the four societies.

CONCLUSIONS

This document is intended to provide genetic and healthcare professionals involved in human reproduction with guidance regarding the clinical implementation of CS.

Genomics and cellulolytic, hemicellulolytic, and amylolytic potential of Iocasia fonsfrigidae strain SP3-1 for polysaccharide degradation

Background

Cellulolytic, hemicellulolytic, and amylolytic (CHA) enzyme-producing halophiles are understudied. The recently defined taxon Iocasia fonsfrigidae consists of one well-described anaerobic bacterial strain: NS-1^T. Prior to characterization of strain NS-1^T, an isolate designated Halocella sp. SP3-1 was isolated and its genome was published. Based on physiological and genetic comparisons, it was suggested that Halocella sp. SP3-1 may be another isolate of I. fronsfrigidae. Despite being geographic variants of the same species, data indicate that strain SP3-1 exhibits genetic, genomic, and physiological characteristics that distinguish it from strain NS-1^T. In this study, we examine the halophilic and alkaliphilic nature of strain SP3-1 and the genetic substrates underlying phenotypic differences between strains SP3-1 and NS-1^T with focus on sugar metabolism and CHA enzyme expression.

Methods

Standard methods in anaerobic cell culture were used to grow strains SP3-1 as well as other comparator species. Morphological characterization was done via electron microscopy and Schaeffer-Fulton staining. Data for sequence comparisons (e.g., 16S rRNA) were retrieved via BLAST and EzBioCloud. Alignments and phylogenetic trees were generated via CLUTAL_X and neighbor joining functions in MEGA (version 11). Genomes were assembled/annotated via the Prokka annotation pipeline. Clusters of Orthologous Groups (COGs) were defined by eegNOG 4.5. DNA-DNA hybridization calculations were performed by the ANI Calculator web service.

Results

Cells of strain SP3-1 are rods. SP3-1 cells grow at NaCl concentrations of 5-30% (w/v). Optimal growth occurs at 37 °C, pH 8.0, and 20% NaCl (w/v). Although phylogenetic analysis based on 16S rRNA gene indicates that strain SP3-1 belongs to the genus Iocasia with 99.58% average nucleotide sequence identity to Iocasia fonsfrigida NS-1^T, strain SP3-1 is uniquely an extreme haloalkaliphile. Moreover, strain SP3-1 ferments D-glucose to acetate, butyrate, carbon dioxide, hydrogen, ethanol, and butanol and will grow on L-arabinose, D-fructose, D-galactose, D-glucose, D-mannose, D-raffinose, D-xylose, cellobiose, lactose, maltose, sucrose, starch, xylan and phosphoric acid swollen cellulose (PASC). D-rhamnose, alginate, and lignin do not serve as suitable culture substrates for strain SP3-1. Thus, the carbon utilization profile of strain SP3-1 differs from that of I. fronsfrigidae strain NS-1^T. Differences between these two strains are also noted in their lipid composition. Genomic data reveal key differences between the genetic profiles of strain SP3-1 and NS-1^T that likely account for differences in morphology, sugar metabolism, and CHA-enzyme potential. Important to this study, I. fonsfrigidae SP3-1 produces and extracellularly secretes CHA enzymes at different levels and composition than type strain NS-1^T. The high salt tolerance and pH range of SP3-1 makes it an ideal candidate for salt and pH tolerant enzyme discovery.

Protein Data Bank: A Comprehensive Review of 3D Structure Holdings and Worldwide Utilization by Researchers, Educators, and Students

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), funded by the United States National Science Foundation, National Institutes of Health, and Department of Energy, supports structural biologists and Protein Data Bank (PDB) data users around the world. The RCSB PDB, a founding member of the Worldwide Protein Data Bank (wwPDB) partnership, serves as the US data center for the global PDB archive housing experimentally-determined three-dimensional (3D) structure data for biological macromolecules. As the wwPDB-designated Archive Keeper, RCSB PDB is also responsible for the security of PDB data and weekly update of the archive. RCSB PDB serves tens of thousands of data depositors (using macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro-electron diffraction) annually working on all permanently inhabited continents. RCSB PDB makes PDB data available from its research-focused web portal at no charge and without usage restrictions to many millions of PDB data consumers around the globe. It also provides educators, students, and the general public with an introduction to the PDB and related training materials through its outreach and education-focused web portal. This review article describes growth of the PDB, examines evolution of experimental methods for structure determination viewed through the lens of the PDB archive, and provides a detailed accounting of PDB archival holdings and their utilization by researchers, educators, and students worldwide.

TargetMine 2022: a new vision into drug target analysis

SUMMARY

We introduce the newest version of TargetMine, which includes the addition of new visualization options; integration of previously disaggregated functionality; and the migration of the front-end to the newly available Bluegenes service.

AVAILABILITY AND IMPLEMENTATION

TargeteMine is accessible online at https://targetmine.mizuguchilab.org/bluegenes. Users do not need to register to use the software. Source code for the different components listed in the article is available from TargetMine's organizational account at http://github.com/targetmine.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

COVID-19 infection and transmission includes complex sequence diversity

SARS-CoV-2 whole genome sequencing has played an important role in documenting the emergence of polymorphisms in the viral genome and its continuing evolution during the COVID-19 pandemic. Here we present data from over 360 patients to characterize the complex sequence diversity of individual infections identified during multiple variant surges (e.g., Alpha and Delta). Across our survey, we observed significantly increasing SARS-CoV-2 sequence diversity during the pandemic and frequent occurrence of multiple biallelic sequence polymorphisms in all infections. This sequence polymorphism shows that SARS-CoV-2 infections are heterogeneous mixtures. Convention for reporting microbial pathogens guides investigators to report a majority consensus sequence. In our study, we found that this approach would under-report sequence variation in all samples tested. As we find that this sequence heterogeneity is efficiently transmitted from donors to recipients, our findings illustrate that infection complexity must be monitored and reported more completely to understand SARS-CoV-2 infection and transmission dynamics. Many of the nucleotide changes that would not be reported in a majority consensus sequence have now been observed as lineage defining SNPs in Omicron BA.1 and/or BA.2 variants. This suggests that minority alleles in earlier SARS-CoV-2 infections may play an important role in the continuing evolution of new variants of concern.

Biological control and plant growth promotion properties of Streptomyces albidoflavus St-220 isolated from Salvia miltiorrhiza rhizosphere

Root rot disease caused by Fusarium oxysporum is a devastating disease of Salvia miltiorrhiza and dramatically affected the production and quality of Sa. miltiorrhiza. Besides the agricultural and chemical control, biocontrol agents can be utilized as an additional solution. In the present study, an actinomycete that highly inhibited F. oxysporum was isolated from rhizosphere soil and identified as based on morphological and molecular characteristics. Greenhouse assay proved that the strain had significant biological control effect against Sa. miltiorrhiza root rot disease and growth-promoting properties on Sa. miltiorrhiza seedlings. To elucidate the biocontrol and plant growth-promoting properties of St-220, we employed an analysis combining genome mining and metabolites detection. Our analyses based on genome sequence and bioassays revealed that the inhibitory activity of St-220 against F. oxysporum was associated with the production of enzymes targeting fungal cell wall and metabolites with antifungal activities. Strain St-220 possesses phosphate solubilization activity, nitrogen fixation activity, siderophore and indole-3-acetic acid production activity in vitro, which may promote the growth of Sa. miltiorrhiza seedlings. These results suggest that St. albidoflavus St-220 is a promising biocontrol agent and also a biofertilizer that could be used in the production of Sa. miltiorrhiza.

Plant growth-promoting properties of Streptomyces spp. isolates and their impact on mung bean plantlets’ rhizosphere microbiome

Phytophthora is an important, highly destructive pathogen of many plants, which causes considerable crop loss, especially durians in Thailand. In this study, we selectively isolated Streptomyces from the rhizosphere soil with a potent anti-oomycete activity against Phytophthora palmivora CbP03. Two strains (SNN087 and SNN289) demonstrated exceptional plant growth-promoting properties in pot experiment. Both strains promoted mung bean (Vigna radiate) growth effectively in both sterile and non-sterile soils. Metagenomic analysis revealed that Streptomyces sp. SNN289 may modify the rhizosphere microbial communities, especially promoting microbes beneficial for plant growth. The relative abundance of bacterial genera Bacillus, Sphingomonas, Arthrobacter, and Pseudarthrobacter, and fungal genera Coprinellus and Chaetomium were noticeably increased, whereas a genus Fusarium was slightly reduced. Interestingly, Streptomyces sp. SNN289 exhibited an exploratory growth, which allows it to survive in a highly competitive environment. Based on whole genome sequence analysis combined with an ANI and dDDH values, this strain should be classifiable as a new species. Functional annotation was also used to characterize plant-beneficial genes in SNN087 and SNN289 genomes for production of siderophores, 3-indole acetic acid (IAA), ammonia, and solubilized phosphate. AntiSMASH genome analysis and preliminary annotation revealed biosynthetic gene clusters with possible secondary metabolites. These findings emphasize the potential for application of strain SNN289 as a bioinoculant for sustainable agricultural practice.

Construction and contextualization approaches for protein-protein interaction networks

Protein-protein interaction network (PPIN) analysis is a widely used method to study the contextual role of proteins of interest, to predict novel disease genes, disease or functional modules, and to identify novel drug targets. PPIN-based analysis uses both generic and context-specific networks. Multiple contextualization methodologies have been described, such as shortest-path algorithms, neighborhood-based methods, and diffusion/propagation algorithms. This review discusses these methods, provides intuitive representations of PPIN contextualization, and also examines how the quality of such context-specific networks could be improved by considering additional sources of evidence. As a heuristic, we observe that tasks such as identifying disease genes, drug targets, and protein complexes should consider local neighborhoods, while uncovering disease mechanisms and discovering disease-pathways would gain from diffusion-based construction.