Diversity of bacterial communities in wetlands of Calakmul Biosphere Reserve: a comparative analysis between conserved and semi-urbanized zones in pre-Mayan Train era.

BACKGROUND: The Calakmul Biosphere Reserve (CBR) is known for its rich animal and plant biodiversity, yet its microbial communities remain largely unknown. The reserve does not possess permanent bodies of water; nevertheless, seasonal depressions associated with fractures create wetlands, known locally as aguadas. Given the recent construction of the Maya train that crosses the CRB, it is essential to assess the biodiversity of its microorganisms and recognize their potential as a valuable source of goods. This evaluation is pivotal in mitigating potential mismanagement of the forest ecosystem. To enhance comprehension of microbial communities, we characterized the microbiota in three different wetlands. Ag-UD1 and Ag-UD2 wetlands are located in a zone without human disturbances, while the third, Ag-SU3, is in a semi-urbanized zone. Sampling was carried out over three years (2017, 2018, and 2019), enabling the monitoring of spatiotemporal variations in bacterial community diversity. The characterization of microbiome composition was conducted using 16S rRNA metabarcoding. Concurrently, the genomic potential of select samples was examined through shotgun metagenomics. RESULTS: Statistical analysis of alpha and beta diversity indices showed significant differences among the bacterial communities found in undisturbed sites Ag-UD1 and Ag-UD2 compared to Ag-SU3. However, no significant differences were observed among sites belonging to the undisturbed area. Furthermore, a comparative analysis at the zone level reveals substantial divergence among the communities, indicating that the geographic location of the samples significantly influences these patterns. The bacterial communities in the CBR wetlands predominantly consist of genera from phyla Actinobacteria, Acidobacteria, and Proteobacteria. CONCLUSION: This characterization has identified the composition of microbial communities and provided the initial overview of the metabolic capacities of the microbiomes inhabiting the aguadas across diverse conservation zones. The three sites exhibit distinct microbial compositions, suggesting that variables such as chemical composition, natural and anthropogenic disturbances, vegetation, and fauna may play a pivotal role in determining the microbial structure of the aguadas. This study establishes a foundational baseline for evaluating the impact of climatic factors and human interventions on critical environments such as wetlands.

Closing the computational biology 'knowledge gap': Spanish Wikipedia as a case study.

MOTIVATION: Wikipedia is a vital open educational resource in computational biology. The quality of computational biology coverage in English-language Wikipedia has improved steadily in recent years. However, there is an increasingly large 'knowledge gap' between computational biology resources in English-language Wikipedia, and Wikipedias in non-English languages. Reducing this knowledge gap by providing educational resources in non-English languages would reduce language barriers which disadvantage non-native English speaking learners across multiple dimensions in computational biology. RESULTS: Here, we provide a comprehensive assessment of computational biology coverage in Spanish-language Wikipedia, the second most accessed Wikipedia worldwide. Using Spanish-language Wikipedia as a case study, we generate quantitative and qualitative data before and after a targeted educational event, specifically, a Spanish-focused student editing competition. Our data demonstrates how such events and activities can narrow the knowledge gap between English and non-English educational resources, by improving existing articles and creating new articles. Finally, based on our analysis, we suggest ways to prioritize future initiatives to improve open educational resources in other languages. AVAILABILITY AND IMPLEMENTATION: Scripts for data analysis are available at: https://github.com/ISCBWikiTeam/spanish.

RNA-Seq Data Analysis: A Practical Guide for Model and Non-Model Organisms.

RNA sequencing (RNA-seq) has emerged as a powerful tool for assessing genome-wide gene expression, revolutionizing various fields of biology. However, analyzing large RNA-seq datasets can be challenging, especially for students or researchers lacking bioinformatics experience. To address these challenges, we present a comprehensive guide to provide step-by-step workflows for analyzing RNA-seq data, from raw reads to functional enrichment analysis, starting with considerations for experimental design. This is designed to aid students and researchers working with any organism, irrespective of whether an assembled genome is available. Within this guide, we employ various recognized bioinformatics tools to navigate the landscape of RNA-seq analysis and discuss the advantages and disadvantages of different tools for the same task. Our protocol focuses on clarity, reproducibility, and practicality to enable users to navigate the complexities of RNA-seq data analysis easily and gain valuable biological insights from the datasets. Additionally, all scripts and a sample dataset are available in a GitHub repository to facilitate the implementation of the analysis pipeline. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Analysis of data from a model plant with an available reference genome Basic Protocol 2: Gene ontology enrichment analysis Basic Protocol 3: De novo assembly of data from non-model plants.

Actinomycetota bioprospecting from ore-forming environments.

Natural products from Actinomycetota have served as inspiration for many clinically relevant therapeutics. Despite early triumphs in natural product discovery, the rate of unearthing new compounds has decreased, necessitating inventive approaches. One promising strategy is to explore environments where survival is challenging. These harsh environments are hypothesized to lead to bacteria developing chemical adaptations (e.g. natural products) to enable their survival. This investigation focuses on ore-forming environments, particularly fluoride mines, which typically have extreme pH, salinity and nutrient scarcity. Herein, we have utilized metagenomics, metabolomics and evolutionary genome mining to dissect the biodiversity and metabolism in these harsh environments. This work has unveiled the promising biosynthetic potential of these bacteria and has demonstrated their ability to produce bioactive secondary metabolites. This research constitutes a pioneering endeavour in bioprospection within fluoride mining regions, providing insights into uncharted microbial ecosystems and their previously unexplored natural products.

Phylogenetic classification of natural product biosynthetic gene clusters based on regulatory mechanisms.

The natural products (NPs) biosynthetic gene clusters (BGCs) represent the adapting biochemical toolkit for microorganisms to thrive different microenvironments. Despite their high diversity, particularly at the genomic level, detecting them in a shake-flask is challenging and remains the primary obstacle limiting our access to valuable chemicals. Studying the molecular mechanisms that regulate BGC expression is crucial to design of artificial conditions that derive on their expression. Here, we propose a phylogenetic analysis of regulatory elements linked to biosynthesis gene clusters, to classify BGCs to regulatory mechanisms based on protein domain information. We utilized Hidden Markov Models from the Pfam database to retrieve regulatory elements, such as histidine kinases and transcription factors, from BGCs in the MIBiG database, focusing on actinobacterial strains from three distinct environments: oligotrophic basins, rainforests, and marine environments. Despite the environmental variations, our isolated microorganisms share similar regulatory mechanisms, suggesting the potential to activate new BGCs using activators known to affect previously characterized BGCs.

Biosynthetic gene profiling and genomic potential of the novel photosynthetic marine bacterium Roseibaca domitiana.

Shifting the bioprospecting targets toward underexplored bacterial groups combined with genome mining studies contributes to avoiding the rediscovery of known compounds by revealing novel, promising biosynthetic gene clusters (BGCs). With the aim of determining the biosynthetic potential of a novel marine bacterium, strain V10T, isolated from the Domitian littoral in Italy, a comparative phylogenomic mining study was performed across related photosynthetic bacterial groups from an evolutionary perspective. Studies on polyphasic and taxogenomics showed that this bacterium constitutes a new species, designated Roseibaca domitiana sp. nov. To date, this genus has only one other validly described species, which was isolated from a hypersaline Antarctic lake. The genomic evolutionary study linked to BGC diversity revealed that there is a close relationship between the phylogenetic distance of the members of the photosynthetic genera Roseibaca, Roseinatronobacter, and Rhodobaca and their BGC profiles, whose conservation pattern allows discriminating between these genera. On the contrary, the rest of the species related to Roseibaca domitiana exhibited an individual species pattern unrelated to genome size or source of isolation. This study showed that photosynthetic strains possess a streamlined content of BGCs, of which 94.34% of the clusters with biotechnological interest (NRPS, PKS, RRE, and RiPP) are completely new. Among these stand out T1PKS, exclusive of R. domitiana V10T, and RRE, highly conserved only in R. domitiana V10T and R. ekhonensis, both categories of BGCs involved in the synthesis of plant growth-promoting compounds and antitumoral compounds, respectively. In all cases, with very low homology with already patented molecules. Our findings reveal the high biosynthetic potential of infrequently cultured bacterial groups, suggesting the need to redirect attention to microbial minorities as a novel and vast source of bioactive compounds still to be exploited.

Omicron-BA.1 Dispersion Rates in Mexico Varied According to the Regional Epidemic Patterns and the Diversity of Local Delta Subvariants.

PURPOSE: The Omicron subvariant BA.1 of SARS-CoV-2 was first detected in November 2021 and quickly spread worldwide, displacing the Delta variant. In this work, a characterization of the spread of this variant in Mexico is presented. METHODS: The time to fixation of BA.1, the diversity of Delta sublineages, the population density, and the level of virus circulation during the inter-wave interval were determined to analyze differences in BA.1 spread. RESULTS: BA.1 began spreading during the first week of December 2021 and became dominant in the next three weeks, causing the fourth COVID-19 epidemiological surge in Mexico. Unlike previous variants, BA.1 did not exhibit a geographically distinct circulation pattern. However, a regional difference in the speed of the replacement of the Delta variant was observed. CONCLUSIONS: Viral diversity and the relative abundance of the virus in a particular area around the time of the introduction of a new lineage seem to have influenced the spread dynamics, in addition to population density. Nonetheless, if there is a significant difference in the fitness of the variants, or if the time allowed for the competition is sufficiently long, it seems the fitter virus will eventually become dominant, as observed in the eventual dominance of the BA.1.x variant in Mexico.

MIBiG 3.0: a community-driven effort to annotate experimentally validated biosynthetic gene clusters.

With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/.

ActinoBase: tools and protocols for researchers working on Streptomyces and other filamentous actinobacteria.

Actinobacteria is an ancient phylum of Gram-positive bacteria with a characteristic high GC content to their DNA. The ActinoBase Wiki is focused on the filamentous actinobacteria, such as Streptomyces species, and the techniques and growth conditions used to study them. These organisms are studied because of their complex developmental life cycles and diverse specialised metabolism which produces many of the antibiotics currently used in the clinic. ActinoBase is a community effort that provides valuable and freely accessible resources, including protocols and practical information about filamentous actinobacteria. It is aimed at enabling knowledge exchange between members of the international research community working with these fascinating bacteria. ActinoBase is an anchor platform that underpins worldwide efforts to understand the ecology, biology and metabolic potential of these organisms. There are two key differences that set ActinoBase apart from other Wiki-based platforms: [1] ActinoBase is specifically aimed at researchers working on filamentous actinobacteria and is tailored to help users overcome challenges working with these bacteria and [2] it provides a freely accessible resource with global networking opportunities for researchers with a broad range of experience in this field.

Metagenomics Bioinformatic Pipeline.

Microbial communities' taxonomic and functional diversity has been broadly studied since sequencing technologies enabled faster and cheaper data obtainment. Nevertheless, the programming skills needed and the amount of software available may be overwhelming to someone trying to analyze these data. Here, we present a comprehensive and straightforward pipeline that takes shotgun metagenomics data through the needed steps to obtain valuable results. The raw data goes through a quality control process, metagenomic assembly, binning (the obtention of single genomes from a metagenome), taxonomic assignment, and taxonomic diversity analysis and visualization.

Dominance of Three Sublineages of the SARS-CoV-2 Delta Variant in Mexico.

In this study, we analyzed the sequences of SARS-CoV-2 isolates of the Delta variant in Mexico, which has completely replaced other previously circulating variants in the country due to its transmission advantage. Among all the Delta sublineages that were detected, 81.5 % were classified as AY.20, AY.26, and AY.100. According to publicly available data, these only reached a world prevalence of less than 1%, suggesting a possible Mexican origin. The signature mutations of these sublineages are described herein, and phylogenetic analyses and haplotype networks are used to track their spread across the country. Other frequently detected sublineages include AY.3, AY.62, AY.103, and AY.113. Over time, the main sublineages showed different geographical distributions, with AY.20 predominant in Central Mexico, AY.26 in the North, and AY.100 in the Northwest and South/Southeast. This work describes the circulation, from May to November 2021, of the primary sublineages of the Delta variant associated with the third wave of the COVID-19 pandemic in Mexico and highlights the importance of SARS-CoV-2 genomic surveillance for the timely identification of emerging variants that may impact public health.

Evolutionary Genome Mining for the Discovery and Engineering of Natural Product Biosynthesis.

Genome mining has become an invaluable tool in natural products research to quickly identify and characterize the biosynthetic pathways that assemble secondary or specialized metabolites. Recently, evolutionary principles have been incorporated into genome mining strategies in an effort to better assess and prioritize novelty and understand their chemical diversification for engineering purposes. Here, we provide an introduction to the principles underlying evolutionary genome mining, including bioinformatic strategies and natural product biosynthetic databases. We introduce workflows for traditional genome mining, focusing on the popular pipeline antiSMASH, and methods to predict enzyme substrate specificity from genomic information. We then provide an in-depth discussion of evolutionary genome mining workflows, including EvoMining, CORASON, ARTS, and others, as adopted by our group for the discovery and prioritization of natural products biosynthetic gene clusters and their products.

The Alpha Variant (B.1.1.7) of SARS-CoV-2 Failed to Become Dominant in Mexico.

During the coronavirus disease 2019 (COVID-19) pandemic, the emergence and rapid increase of the B.1.1.7 (Alpha) lineage of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), first identified in the United Kingdom in September 2020, was well documented in different areas of the world and became a global public health concern because of its increased transmissibility. The B.1.1.7 lineage was first detected in Mexico during December 2020, showing a slow progressive increase in its circulation frequency, which reached its maximum in May 2021 but never became predominant. In this work, we analyzed the patterns of diversity and distribution of this lineage in Mexico using phylogenetic and haplotype network analyses. Despite the reported increase in transmissibility of the B.1.1.7 lineage, in most Mexican states, it did not displace cocirculating lineages, such as B.1.1.519, which dominated the country from February to May 2021. Our results show that the states with the highest prevalence of B.1.1.7 were those at the Mexico-U.S. border. An apparent pattern of dispersion of this lineage from the northern states of Mexico toward the center or the southeast was observed in the largest transmission chains, indicating possible independent introduction events from the United States. However, other entry points cannot be excluded, as shown by multiple introduction events. Local transmission led to a few successful haplotypes with a localized distribution and specific mutations indicating sustained community transmission. IMPORTANCE The emergence and rapid increase of the B.1.1.7 (Alpha) lineage of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) throughout the world were due to its increased transmissibility. However, it did not displace cocirculating lineages in most of Mexico, particularly B.1.1.519, which dominated the country from February to May 2021. In this work, we analyzed the distribution of B.1.1.7 in Mexico using phylogenetic and haplotype network analyses. Our results show that the states with the highest prevalence of B.1.1.7 (around 30%) were those at the Mexico-U.S. border, which also exhibited the highest lineage diversity, indicating possible introduction events from the United States. Also, several haplotypes were identified with a localized distribution and specific mutations, indicating that sustained community transmission occurred in the country.

Unveiling the genomic potential of Pseudomonas type strains for discovering new natural products.

Microbes host a huge variety of biosynthetic gene clusters that produce an immeasurable array of secondary metabolites with many different biological activities such as antimicrobial, anticarcinogenic and antiviral. Despite the complex task of isolating and characterizing novel natural products, microbial genomic strategies can be useful for carrying out these types of studies. However, although genomic-based research on secondary metabolism is on the increase, there is still a lack of reports focusing specifically on the genus Pseudomonas. In this work, we aimed (i) to unveil the main biosynthetic systems related to secondary metabolism in Pseudomonas type strains, (ii) to study the evolutionary processes that drive the diversification of their coding regions and (iii) to select Pseudomonas strains showing promising results in the search for useful natural products. We performed a comparative genomic study on 194 Pseudomonas species, paying special attention to the evolution and distribution of different classes of biosynthetic gene clusters and the coding features of antimicrobial peptides. Using EvoMining, a bioinformatic approach for studying evolutionary processes related to secondary metabolism, we sought to decipher the protein expansion of enzymes related to the lipid metabolism, which may have evolved toward the biosynthesis of novel secondary metabolites in Pseudomonas. The types of metabolites encoded in Pseudomonas type strains were predominantly non-ribosomal peptide synthetases, bacteriocins, N-acetylglutaminylglutamine amides and ß-lactones. Also, the evolution of genes related to secondary metabolites was found to coincide with Pseudomonas species diversification. Interestingly, only a few Pseudomonas species encode polyketide synthases, which are related to the lipid metabolism broadly distributed among bacteria. Thus, our EvoMining-based search may help to discover new types of secondary metabolite gene clusters in which lipid-related enzymes are involved. This work provides information about uncharacterized metabolites produced by Pseudomonas type strains, whose gene clusters have evolved in a species-specific way. Our results provide novel insight into the secondary metabolism of Pseudomonas and will serve as a basis for the prioritization of the isolated strains. This article contains data hosted by Microreact.

Genomic study and lipidomic bioassay of Leeuwenhoekiella parthenopeia: A novel rare biosphere marine bacterium that inhibits tumor cell viability.

The fraction of low-abundance microbiota in the marine environment is a promising target for discovering new bioactive molecules with pharmaceutical applications. Phenomena in the ocean such as diel vertical migration (DVM) and seasonal dynamic events influence the pattern of diversity of marine bacteria, conditioning the probability of isolation of uncultured bacteria. In this study, we report a new marine bacterium belonging to the rare biosphere, Leeuwenhoekiella parthenopeia sp. nov. Mr9T, which was isolated employing seasonal and diel sampling approaches. Its complete characterization, ecology, biosynthetic gene profiling of the whole genus Leeuwenhoekiella, and bioactivity of its extract on human cells are reported. The phylogenomic and microbial diversity studies demonstrated that this bacterium is a new and rare species, barely representing 0.0029% of the bacterial community in Mediterranean Sea metagenomes. The biosynthetic profiling of species of the genus Leeuwenhoekiella showed nine functionally related gene cluster families (GCF), none were associated with pathways responsible to produce known compounds or registered patents, therefore revealing its potential to synthesize novel bioactive compounds. In vitro screenings of L. parthenopeia Mr9T showed that the total lipid content (lipidome) of the cell membrane reduces the prostatic and brain tumor cell viability with a lower effect on normal cells. The lipidome consisted of sulfobacin A, WB 3559A, WB 3559B, docosenamide, topostin B-567, and unknown compounds. Therefore, the bioactivity could be attributed to any of these individual compounds or due to their synergistic effect. Beyond the rarity and biosynthetic potential of this bacterium, the importance and novelty of this study is the employment of sampling strategies based on ecological factors to reach the hidden microbiota, as well as the use of bacterial membrane constituents as potential novel therapeutics. Our findings open new perspectives on cultivation and the relationship between bacterial biological membrane components and their bioactivity in eukaryotic cells, encouraging similar studies in other members of the rare biosphere.

The confluence of big data and evolutionary genome mining for the discovery of natural products.

This review covers literature between 2003-2021The development and application of genome mining tools has given rise to ever-growing genetic and chemical databases and propelled natural products research into the modern age of Big Data. Likewise, an explosion of evolutionary studies has unveiled genetic patterns of natural products biosynthesis and function that support Darwin's theory of natural selection and other theories of adaptation and diversification. In this review, we aim to highlight how Big Data and evolutionary thinking converge in the study of natural products, and how this has led to an emerging sub-discipline of evolutionary genome mining of natural products. First, we outline general principles to best utilize Big Data in natural products research, addressing key considerations needed to provide evolutionary context. We then highlight successful examples where Big Data and evolutionary analyses have been combined to provide bioinformatic resources and tools for the discovery of novel natural products and their biosynthetic enzymes. Rather than an exhaustive list of evolution-driven discoveries, we highlight examples where Big Data and evolutionary thinking have been embraced for the evolutionary genome mining of natural products. After reviewing the nascent history of this sub-discipline, we discuss the challenges and opportunities of genomic and metabolomic tools with evolutionary foundations and/or implications and provide a future outlook for this emerging and exciting field of natural product research.

Integrating perspectives in actinomycete research: an ActinoBase review of 2020-21.

Last year ActinoBase, a Wiki-style initiative supported by the UK Microbiology Society, published a review highlighting the research of particular interest to the actinomycete community. Here, we present the second ActinoBase review showcasing selected reports published in 2020 and early 2021, integrating perspectives in the actinomycete field. Actinomycetes are well-known for their unsurpassed ability to produce specialised metabolites, of which many are used as therapeutic agents with antibacterial, antifungal, or immunosuppressive activities. Much research is carried out to understand the purpose of these metabolites in the environment, either within communities or in host interactions. Moreover, many efforts have been placed in developing computational tools to handle big data, simplify experimental design, and find new biosynthetic gene cluster prioritisation strategies. Alongside, synthetic biology has provided advances in tools to elucidate the biosynthesis of these metabolites. Additionally, there are still mysteries to be uncovered in understanding the fundamentals of filamentous actinomycetes' developmental cycle and regulation of their metabolism. This review focuses on research using integrative methodologies and approaches to understand the bigger picture of actinomycete biology, covering four research areas: i) technology and methodology; ii) specialised metabolites; iii) development and regulation; and iv) ecology and host interactions.

Catalytically impaired TrpA subunit of tryptophan synthase from Chlamydia trachomatis is an allosteric regulator of TrpB.

Intracellular growth and pathogenesis of Chlamydia species is controlled by the availability of tryptophan, yet the complete biosynthetic pathway for l-Trp is absent among members of the genus. Some representatives, however, preserve genes encoding tryptophan synthase, TrpAB - a bifunctional enzyme catalyzing the last two steps in l-Trp synthesis. TrpA (subunit α) converts indole-3-glycerol phosphate into indole and glyceraldehyde-3-phosphate (α reaction). The former compound is subsequently used by TrpB (subunit ß) to produce l-Trp in the presence of l-Ser and a pyridoxal 5'-phosphate cofactor (ß reaction). Previous studies have indicated that in Chlamydia, TrpA has lost its catalytic activity yet remains associated with TrpB to support the ß reaction. Here, we provide detailed analysis of the TrpAB from C. trachomatis D/UW-3/CX, confirming that accumulation of mutations in the active site of TrpA renders it enzymatically inactive, despite the conservation of the catalytic residues. We also show that TrpA remains a functional component of the TrpAB complex, increasing the activity of TrpB by four-fold. The side chain of non-conserved ßArg267 functions as cation effector, potentially rendering the enzyme less susceptible to the solvent ion composition. The observed structural and functional changes detected herein were placed in a broader evolutionary and genomic context, allowing identification of these mutations in relation to their trp gene contexts in which they occur. Moreover, in agreement with the in vitro data, partial relaxation of purifying selection for TrpA, but not for TrpB, was detected, reinforcing a partial loss of TrpA functions during the course of evolution.

ActDES - a curated Actinobacterial Database for Evolutionary Studies.

Actinobacteria is a large and diverse phylum of bacteria that contains medically and ecologically relevant organisms. Many members are valuable sources of bioactive natural products and chemical precursors that are exploited in the clinic and made using the enzyme pathways encoded in their complex genomes. Whilst the number of sequenced genomes has increased rapidly in the last 20 years, the large size, complexity and high G+C content of many actinobacterial genomes means that the sequences remain incomplete and consist of large numbers of contigs with poor annotation, which hinders large-scale comparative genomic and evolutionary studies. To enable greater understanding and exploitation of actinobacterial genomes, specialized genomic databases must be linked to high-quality genome sequences. Here, we provide a curated database of 612 high-quality actinobacterial genomes from 80 genera, chosen to represent a broad phylogenetic group with equivalent genome re-annotation. Utilizing this database will provide researchers with a framework for evolutionary and metabolic studies, to enable a foundation for genome and metabolic engineering, to facilitate discovery of novel bioactive therapeutics and studies on gene family evolution. This article contains data hosted by Microreact.

Evolutionary dynamics of natural product biosynthesis in bacteria.

Covering: 2008 up to 2019The forces of biochemical adaptive evolution operate at the level of genes, manifesting in complex phenotypes and the global biodiversity of proteins and metabolites. While evolutionary histories have been deciphered for some other complex traits, the origins of natural product biosynthesis largely remain a mystery. This fundamental knowledge gap is surprising given the many decades of research probing the genetic, chemical, and biophysical mechanisms of bacterial natural product biosynthesis. Recently, evolutionary thinking has begun to permeate this otherwise mechanistically dominated field. Natural products are now sometimes referred to as 'specialized' rather than 'secondary' metabolites, reinforcing the importance of their biological and ecological functions. Here, we review known evolutionary mechanisms underlying the overwhelming chemical diversity of bacterial secondary metabolism, focusing on enzyme promiscuity and the evolution of enzymatic domains that enable metabolic traits. We discuss the mechanisms that drive the assembly of natural product biosynthetic gene clusters and propose formal definitions for 'specialized' and 'secondary' metabolism. We further explore how biosynthetic gene clusters evolve to synthesize related molecular species, and in turn how the biological and ecological roles that emerge from metabolic diversity are acted on by selection. Finally, we reconcile chemical, functional, and genetic data into an evolutionary model, the dynamic chemical matrix evolutionary hypothesis, in which the relationships between chemical distance, biomolecular activity, and relative fitness shape adaptive landscapes.