Pollution-related changes in nest microbiota: Implications for growth and fledging in three passerine birds.

Non-ferrous smelters emit toxic metals into the environment, posing a threat to wildlife health. Despite the acknowledged role of microbes in host health, the impact of such emissions on host-associated microbiota, especially in wild birds, remains largely unexplored. This study investigates the associations of metal pollution, fitness, and nest microbiota (serving as a proxy for early-life microbial environment) which may influence the nestling health and development. Our study focuses on three passerine birds, the great tit (Parus major), blue tit (Cyanistes caeruleus), and pied flycatcher (Ficedula hypoleuca), within control and metal-polluted sites around a Finnish copper-nickel smelter. The polluted sites had been contaminated with arsenic (As), cadmium (Cd), copper (Cu), nickel (Ni), and zinc (Zn). We performed bacterial 16S rRNA sequencing and metal analyses on 90 nests and monitored nestling body mass, fledging success, and various biotic and abiotic factors. Our findings revealed species-specific responses to metal exposure in terms of both fitness and nest microbiota. P. major and C. caeruleus showed sensitivity to pollution, with decreased nestling growth and fledging in the polluted zone. This was accompanied by a shift in the bacterial community composition, which was characterized by an increase in some pathogenic bacteria (in P. major and C. caeruleus nests) and by a decrease in plant-associated bacteria (within C. caeruleus nests). Conversely, F. hypoleuca and their nest microbiota showed limited responses to pollution, indicating greater tolerance to pollution-induced environmental changes. Although pollution did not correlate with nest alpha diversity or the most abundant bacterial taxa across all species, certain potential pathogens within the nests were enriched in polluted environments and negatively correlated with nestling fitness parameters. Our results suggest that metal pollution may alter the nest bacterial composition in some bird species, either directly or indirectly through environmental changes, promoting pathogenic bacteria and potentially impacting bird survival.

Use of the Phylobone database for the annotation of bone extracellular matrix proteins in reindeer (Rangifer tarandus).

Bone extracellular matrix (ECM) proteins play a key role in bone formation and regeneration, including structural and regulatory functions. The Phylobone database consists of 255 ECM protein groups from 39 species and can be used to support bone research. Here, we gathered bone ECM proteins from reindeer (Rangifer tarandus), a member of the Cervidae family. The importance of reindeer lies in their ability to regenerate their antlers, in both male and female individuals. Protein sequences were extracted from the National Center for Biotechnology Information's repository and selected by homology searches. We identified 215 proteins and their corresponding functional domains, which are putatively present in the bone ECM of reindeer. Protein sequence alignments have shown a high degree of conservation between R. tarandus and other members of the Cervidae family. This update expands the Phylobone database and shows that it is a useful resource for the preliminary annotation of bone ECM proteins in novel proteomes.

Phylobone: a comprehensive database of bone extracellular matrix proteins in human and model organisms.

The bone extracellular matrix (ECM) contains minerals deposited on highly crosslinked collagen fibrils and hundreds of non-collagenous proteins. Some of these proteins are key to the regulation of bone formation and regeneration via signaling pathways, and play important regulatory and structural roles. However, the complete list of bone extracellular matrix proteins, their roles, and the extent of individual and cross-species variations have not been fully captured in both humans and model organisms. Here, we introduce the most comprehensive resource of bone extracellular matrix (ECM) proteins that can be used in research fields such as bone regeneration, osteoporosis, and mechanobiology. The Phylobone database (available at https://phylobone.com ) includes 255 proteins potentially expressed in the bone extracellular matrix (ECM) of humans and 30 species of vertebrates. A bioinformatics pipeline was used to identify the evolutionary relationships of bone ECM proteins. The analysis facilitated the identification of potential model organisms to study the molecular mechanisms of bone regeneration. A network analysis showed high connectivity of bone ECM proteins. A total of 214 functional protein domains were identified, including collagen and the domains involved in bone formation and resorption. Information from public drug repositories was used to identify potential repurposing of existing drugs. The Phylobone database provides a platform to study bone regeneration and osteoporosis in light of (biological) evolution, and will substantially contribute to the identification of molecular mechanisms and drug targets.

Glyphosate and a glyphosate-based herbicide affect bumblebee gut microbiota.

Pollinator decline is one of the gravest challenges facing the world today, and the overuse of pesticides may be among its causes. Here, we studied whether glyphosate, the world's most widely used pesticide, affects the bumblebee gut microbiota. We exposed the bumblebee diet to glyphosate and a glyphosate-based herbicide and quantified the microbiota community shifts using 16S rRNA gene sequencing. Furthermore, we estimated the potential sensitivity of bee gut microbes to glyphosate based on previously reported presence of target enzyme. Glyphosate increased, whereas the glyphosate-based herbicide decreased gut microbiota diversity, indicating that negative effects are attributable to co-formulants. Both glyphosate and the glyphosate-based herbicide treatments significantly decreased the relative abundance of potentially glyphosate-sensitive bacterial species Snodgrasella alvi. However, the relative abundance of potentially glyphosate-sensitive Candidatus Schmidhempelia genera increased in bumblebees treated with glyphosate. Overall, 50% of the bacterial genera detected in the bee gut microbiota were classified as potentially resistant to glyphosate, while 36% were classified as sensitive. Healthy core microbiota have been shown to protect bees from parasite infections, change metabolism, and decrease mortality. Thus, the heavy use of glyphosate-based herbicides may have implications on bees and ecosystems.

The Mutational Landscape of SARS-CoV-2.

Mutation research is crucial for detecting and treating SARS-CoV-2 and developing vaccines. Using over 5,300,000 sequences from SARS-CoV-2 genomes and custom Python programs, we analyzed the mutational landscape of SARS-CoV-2. Although almost every nucleotide in the SARS-CoV-2 genome has mutated at some time, the substantial differences in the frequency and regularity of mutations warrant further examination. C>U mutations are the most common. They are found in the largest number of variants, pangolin lineages, and countries, which indicates that they are a driving force behind the evolution of SARS-CoV-2. Not all SARS-CoV-2 genes have mutated in the same way. Fewer non-synonymous single nucleotide variations are found in genes that encode proteins with a critical role in virus replication than in genes with ancillary roles. Some genes, such as spike (S) and nucleocapsid (N), show more non-synonymous mutations than others. Although the prevalence of mutations in the target regions of COVID-19 diagnostic RT-qPCR tests is generally low, in some cases, such as for some primers that bind to the N gene, it is significant. Therefore, ongoing monitoring of SARS-CoV-2 mutations is crucial. The SARS-CoV-2 Mutation Portal provides access to a database of SARS-CoV-2 mutations.

Glyphosate-based herbicide use affects individual microbial taxa in strawberry endosphere but not the microbial community composition.

AIMS: In a field study, the effects of treatments of glyphosate-based herbicides (GBHs) in soil, alone and in combination with phosphate fertilizer, were examined on the performance and endophytic microbiota of garden strawberry. METHODS AND RESULTS: The root and leaf endophytic microbiota of garden strawberries grown in GBH-treated and untreated soil, with and without phosphate fertilizer, were analyzed. Next, bioinformatics analysis on the type of 5-enolpyruvylshikimate-3-phosphate synthase enzyme was conducted to assess the potential sensitivity of strawberry-associated bacteria and fungi to glyphosate, and to compare the results with field observations. GBH treatments altered the abundance and/or frequency of several operational taxonomic units (OTUs), especially those of root-associated fungi and bacteria. These changes were partly related to their sensitivity to glyphosate. Still, GBH treatments did not shape the overall community structure of strawberry microbiota or affect plant performance. Phosphate fertilizer increased the abundance of both glyphosate-resistant and glyphosate-sensitive bacterial OTUs, regardless of the GBH treatments. CONCLUSIONS: These findings demonstrate that although the overall community structure of strawberry endophytic microbes is not affected by GBH use, some individual taxa are.

Ecosystem consequences of herbicides: the role of microbiome.

Non-target organisms are globally exposed to herbicides. While many herbicides - for example, glyphosate - were initially considered safe, increasing evidence demonstrates that they have profound effects on ecosystem functions via altered microbial communities. We provide a comprehensive framework on how herbicide residues may modulate ecosystem-level outcomes via alteration of microbiomes. The changes in soil microbiome are likely to influence key nutrient cycling and plant-soil processes. Herbicide-altered microbiome affects plant and animal performance and can influence trophic interactions such as herbivory and pollination. These changes are expected to lead to ecosystem and even evolutionary consequences for both microbes and hosts. Tackling the threats caused by agrochemicals to ecosystem functions and services requires tools and solutions based on a comprehensive understanding of microbe-mediated risks.

Prediction of Recurrent Mutations in SARS-CoV-2 Using Artificial Neural Networks.

Predicting SARS-CoV-2 mutations is difficult, but predicting recurrent mutations driven by the host, such as those caused by host deaminases, is feasible. We used machine learning to predict which positions from the SARS-CoV-2 genome will hold a recurrent mutation and which mutations will be the most recurrent. We used data from April 2021 that we separated into three sets: a training set, a validation set, and an independent test set. For the test set, we obtained a specificity value of 0.69, a sensitivity value of 0.79, and an Area Under the Curve (AUC) of 0.8, showing that the prediction of recurrent SARS-CoV-2 mutations is feasible. Subsequently, we compared our predictions with updated data from January 2022, showing that some of the false positives in our prediction model become true positives later on. The most important variables detected by the model's Shapley Additive exPlanation (SHAP) are the nucleotide that mutates and RNA reactivity. This is consistent with the SARS-CoV-2 mutational bias pattern and the preference of some host deaminases for specific sequences and RNA secondary structures. We extend our investigation by analyzing the mutations from the variants of concern Alpha, Beta, Delta, Gamma, and Omicron. Finally, we analyzed amino acid changes by looking at the predicted recurrent mutations in the M-pro and spike proteins.

Rethinking the Intrinsic Sensitivity of Fungi to Glyphosate.

The 5-enolpyruvylshikimate 3-phosphate synthase (EPSPS) is the central enzyme of the shikimate pathway to synthesize the three aromatic amino acids in fungi, plants, and prokaryotes. This enzyme is the target of the herbicide glyphosate. In most plants and prokaryotes, the EPSPS protein is constituted by a single domain family, the EPSP synthase (PF00275) domain, whereas in fungi, the protein is formed by a multi-domain structure from combinations of 22 EPSPS-associated domains. The most common multi-domain EPSPS structure in fungi involves five EPSPS-associated domains of the shikimate pathway. In this article, we analyze 390 EPSPS proteins of fungi to determine the extent of the EPSPS-associated domains. Based on the current classification of the EPSPS protein, most fungal species are intrinsically sensitive to glyphosate. However, complex domain architectures may have multiple responses to the herbicide. Further empirical studies are needed to determine the effect of glyphosate on fungi, taking into account the diversity of multi-domain architectures of the EPSPS. This research opens the door to novel biotechnological applications for microbial degradation of glyphosate.

Does Glyphosate Affect the Human Microbiota?

Glyphosate is the world's most widely used agrochemical. Its use in agriculture and gardening has been proclaimed safe because humans and other animals do not have the target enzyme 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS). However, increasing numbers of studies have demonstrated risks to humans and animals because the shikimate metabolic pathway is present in many microbes. Here, we assess the potential effect of glyphosate on healthy human microbiota. Our results demonstrate that more than one-half of human microbiome are intrinsically sensitive to glyphosate. However, further empirical studies are needed to determine the effect of glyphosate on healthy human microbiota.

Why Is the UAG (Amber) Stop Codon Almost Absent in Highly Expressed Bacterial Genes?

The genome hypothesis postulates that genes in a genome tend to conform to their species' usage of the codon catalog and the GC content of the DNA. Thus, codon frequencies differ across organisms, including the three termination codons in the standard genetic code. Here, we analyze the frequencies of stop codons in a group of highly expressed genes from 196 prokaryotes under strong translational selection. The occurrence of the three translation termination codons is highly biased, with UAA (ochre) being the most prevalent in almost all bacteria. In contrast, UAG (amber) is the least frequent termination codon, e.g., only 321 occurrences (7.4%) in E. coli K-12 substr. W3110. Of the 253 highly expressed genes, only two end with an UAG codon. The strength of the selective bias against UAG in highly expressed genes varies among bacterial genomes, but it is not affected by the GC content of these genomes. In contrast, increased GC content results in a decrease in UAA abundance with a concomitant increase in UGA abundance. We propose that readthrough efficiency and context effects could explain the prevalence of UAA over UAG, particularly in highly expressed genes. Findings from this communication can be utilized for the optimization of gene expression.

Quantification of the Potential Impact of Glyphosate-Based Products on Microbiomes.

Glyphosate-based products (GBP) are the most common broad-spectrum herbicides worldwide. The target of glyphosate is the enzyme 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) in the shikimate pathway, which is virtually universal in plants. The inhibition of the enzyme stops the production of three essential amino acids: phenylalanine, tyrosine, and tryptophan. EPSPS is also present in fungi and prokaryotes, such as archaea and bacteria; thus, the use of GBP may have an impact on the microbiome composition of soils, plants, herbivores, and secondary consumers. This article aims to present general guidelines to assess the effect of GBP on microbiomes from field experiments to bioinformatics analyses and provide a few testable hypotheses. Two field experiments are presented to test the GBP on non-target organisms. First, plant-associated microbes from 10 replicated control and GBP treatment plots simulating no-till cropping are sampled and analyzed. In the second experiment, samples from experimental plots fertilized by either poultry manure containing glyphosate residues or non-treated control manure were obtained. Bioinformatics analysis of EPSPS protein sequences is utilized to determine the potential sensitivity of microbes to glyphosate. The first step in estimating the effect of GBP on microbiomes is to determine their potential sensitivity to the target enzyme (EPSPS). Microbial sequences can be obtained either from public repositories or by means of PCR amplification. However, in the majority of field studies, microbiome composition has been determined based on universal DNA markers such as the 16S rRNA and the internal transcribed spacer (ITS). In these cases, sensitivity to glyphosate can only be estimated through a probabilistic analysis of EPSPS sequences using closely related species. The quantification of the potential sensitivity of organisms to glyphosate, based on the EPSPS enzyme, provides a robust approach for further experiments to study target and non-target resistant mechanisms.

Alterations in Metabolome and Microbiome Associated with an Early Stress Stage in Male Wistar Rats: A Multi-Omics Approach.

Stress disorders have dramatically increased in recent decades becoming the most prevalent psychiatric disorder in the United States and Europe. However, the diagnosis of stress disorders is currently based on symptom checklist and psychological questionnaires, thus making the identification of candidate biomarkers necessary to gain better insights into this pathology and its related metabolic alterations. Regarding the identification of potential biomarkers, omic profiling and metabolic footprint arise as promising approaches to recognize early biochemical changes in such disease and provide opportunities for the development of integrative candidate biomarkers. Here, we studied plasma and urine metabolites together with metagenomics in a 3 days Chronic Unpredictable Mild Stress (3d CUMS) animal approach that aims to focus on the early stress period of a well-established depression model. The multi-omics integration showed a profile composed by a signature of eight plasma metabolites, six urine metabolites and five microbes. Specifically, threonic acid, malic acid, alpha-ketoglutarate, succinic acid and cholesterol were proposed as key metabolites that could serve as key potential biomarkers in plasma metabolome of early stages of stress. Such findings targeted the threonic acid metabolism and the tricarboxylic acid (TCA) cycle as important pathways in early stress. Additionally, an increase in opportunistic microbes as virus of the Herpesvirales was observed in the microbiota as an effect of the primary stress stages. Our results provide an experimental biochemical characterization of the early stage of CUMS accompanied by a subsequent omic profiling and a metabolic footprinting that provide potential candidate biomarkers.

An Unsupervised Algorithm for Host Identification in Flaviviruses.

Early characterization of emerging viruses is essential to control their spread, such as the Zika Virus outbreak in 2014. Among other non-viral factors, host information is essential for the surveillance and control of virus spread. Flaviviruses (genus Flavivirus), akin to other viruses, are modulated by high mutation rates and selective forces to adapt their codon usage to that of their hosts. However, a major challenge is the identification of potential hosts for novel viruses. Usually, potential hosts of emerging zoonotic viruses are identified after several confirmed cases. This is inefficient for deterring future outbreaks. In this paper, we introduce an algorithm to identify the host range of a virus from its raw genome sequences. The proposed strategy relies on comparing codon usage frequencies across viruses and hosts, by means of a normalized Codon Adaptation Index (CAI). We have tested our algorithm on 94 flaviviruses and 16 potential hosts. This novel method is able to distinguish between arthropod and vertebrate hosts for several flaviviruses with high values of accuracy (virus group 91.9% and host type 86.1%) and specificity (virus group 94.9% and host type 79.6%), in comparison to empirical observations. Overall, this algorithm may be useful as a complementary tool to current phylogenetic methods in monitoring current and future viral outbreaks by understanding host-virus relationships.

A Pilot Study for Metabolic Profiling of Obesity-Associated Microbial Gut Dysbiosis in Male Wistar Rats.

Obesity is one of the most incident and concerning disease worldwide. Definite strategies to prevent obesity and related complications remain elusive. Among the risk factors of the onset of obesity, gut microbiota might play an important role in the pathogenesis of the disease, and it has received extensive attention because it affects the host metabolism. In this study, we aimed to define a metabolic profile of the segregated obesity-associated gut dysbiosis risk factor. The study of the metabolome, in an obesity-associated gut dysbiosis model, provides a relevant way for the discrimination on the different biomarkers in the obesity onset. Thus, we developed a model of this obesity risk factors through the transference of gut microbiota from obese to non-obese male Wistar rats and performed a subsequent metabolic analysis in the receptor rats. Our results showed alterations in the lipid metabolism in plasma and in the phenylalanine metabolism in urine. In consequence, we have identified metabolic changes characterized by: (1) an increase in DG:34:2 in plasma, a decrease in hippurate, (2) an increase in 3-HPPA, and (3) an increase in o-coumaric acid. Hereby, we propose these metabolites as a metabolic profile associated to a segregated dysbiosis state related to obesity disease.

Adaptation of bacteria to glyphosate: a microevolutionary perspective of the enzyme 5-enolpyruvylshikimate-3-phosphate synthase.

Glyphosate is the leading herbicide worldwide, but it also affects prokaryotes because it targets the central enzyme (5-enolpyruvylshikimate-3-phosphate, EPSP) of the shikimate pathway in the synthesis of the three essential aromatic amino acids in bacteria, fungi and plants. Our results reveal that bacteria may easily become resistant to glyphosate through changes in the 5-enolpyruvylshikimate-3-phosphate synthase active site. This indicates the importance of examining how glyphosate affects microbe-mediated ecosystem functions and human microbiomes.

Classification of the glyphosate target enzyme (5-enolpyruvylshikimate-3-phosphate synthase) for assessing sensitivity of organisms to the herbicide.

Glyphosate is the most common broad-spectrum herbicide. It targets the key enzyme of the shikimate pathway, 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS), which synthesizes three essential aromatic amino acids (phenylalanine, tyrosine and tryptophan) in plants. Because the shikimate pathway is also found in many prokaryotes and fungi, the widespread use of glyphosate may have unsuspected impacts on the diversity and composition of microbial communities, including the human gut microbiome. Here, we introduce the first bioinformatics method to assess the potential sensitivity of organisms to glyphosate based on the type of EPSPS enzyme. We have precomputed a dataset of EPSPS sequences from thousands of species that will be an invaluable resource to advancing the research field. This novel methodology can classify sequences from nearly 90% of eukaryotes and >80% of prokaryotes. A conservative estimate from our results shows that 54% of species in the core human gut microbiome are sensitive to glyphosate.

Genome-Wide Comparative Analysis of Phylogenetic Trees: The Prokaryotic Forest of Life.

Genome-wide comparison of phylogenetic trees is becoming an increasingly common approach in evolutionary genomics, and a variety of approaches for such comparison have been developed. In this article we present several methods for comparative analysis of large numbers of phylogenetic trees. To compare phylogenetic trees taking into account the bootstrap support for each internal branch, the boot-split distance (BSD) method is introduced as an extension of the previously developed split distance (SD) method for tree comparison. The BSD method implements the straightforward idea that comparison of phylogenetic trees can be made more robust by treating tree splits differentially depending on the bootstrap support. Approaches are also introduced for detecting treelike and netlike evolutionary trends in the phylogenetic Forest of Life (FOL), i.e., the entirety of the phylogenetic trees for conserved genes of prokaryotes. The principal method employed for this purpose includes mapping quartets of species onto trees to calculate the support of each quartet topology and so to quantify the tree and net contributions to the distances between species. We describe the applications methods used to analyze the FOL and the results obtained with these methods. These results support the concept of the Tree of Life (TOL) as a central evolutionary trend in the FOL as opposed to the traditional view of the TOL as a "species tree."

Six-Week Endurance Exercise Alters Gut Metagenome That Is not Reflected in Systemic Metabolism in Over-weight Women.

Recent studies suggest that exercise alters the gut microbiome. We determined whether six-weeks endurance exercise, without changing diet, affected the gut metagenome and systemic metabolites of overweight women. Previously sedentary overweight women (n = 19) underwent a six-weeks endurance exercise intervention, but two were excluded due to antibiotic therapy. The gut microbiota composition and functions were analyzed by 16S rRNA gene amplicon sequencing and metagenomics. Body composition was analyzed with DXA X-ray densitometer and serum metabolomics with NMR metabolomics. Total energy and energy-yielding nutrient intakes were analyzed from food records using Micro-Nutrica software. Serum clinical variables were determined with KONELAB instrument. Soluble Vascular Adhesion Protein 1 (VAP-1) was measured with ELISA and its' enzymatic activity as produced hydrogen peroxide. The exercise intervention was effective, as maximal power and maximum rate of oxygen consumption increased while android fat mass decreased. No changes in diet were observed. Metagenomic analysis revealed taxonomic shifts including an increase in Akkermansia and a decrease in Proteobacteria. These changes were independent of age, weight, fat % as well as energy and fiber intake. Training slightly increased Jaccard distance of genus level ß-diversity. Training did not alter the enriched metagenomic pathways, which, according to Bray Curtis dissimilarity analysis, may have been due to that only half of the subjects' microbiomes responded considerably to exercise. Nevertheless, tranining decreased the abundance of several genes including those related to fructose and amino acid metabolism. These metagenomic changes, however, were not translated into major systemic metabolic changes as only two metabolites, phospholipids and cholesterol in large VLDL particles, decreased after exercise. Training also decreased the amine oxidase activity of pro-inflammatory VAP-1, whereas no changes in CRP were detected. All clinical blood variables were within normal range, yet exercise slightly increased glucose and decreased LDL and HDL. In conclusion, exercise training modified the gut microbiome without greatly affecting systemic metabolites or body composition. Based on our data and existing literature, we propose that especially Akkermansia and Proteobacteria are exercise-responsive taxa. Our results warrant the need for further studies in larger cohorts to determine whether exercise types other than endurance exercise also modify the gut metagenome.

Reconstruction of the evolution of microbial defense systems.

BACKGROUND: Evolution of bacterial and archaeal genomes is a highly dynamic process that involves intensive loss of genes as well as gene gain via horizontal transfer, with a lesser contribution from gene duplication. The rates of these processes can be estimated by comparing genomes that are linked by an evolutionary tree. These estimated rates of genome dynamics events substantially differ for different functional classes of genes. The genes involved in defense against viruses and other invading DNA are among those that are gained and lost at the highest rates. RESULTS: We employed a stochastic birth-and-death model to obtain maximum likelihood estimates of the rates of gain and loss of defense genes in 35 groups of closely related bacterial genomes and one group of archaeal genomes. We find that on average, the defense genes experience 1.4 fold higher flux than the rest of microbial genes. This excessive flux of defense genes over the genomic mean is consistent across diverse microbial groups. The few exceptions include intracellular parasites with small, degraded genomes that possess few defense systems which are more stable than in other microbes. Generally, defense genes follow the previously established pattern of genome dynamics, with gene family loss being about 3 times more common than gain and an order of magnitude more common than expansion or contraction of gene families. Case by case analysis of the evolutionary dynamics of defense genes indicates frequent multiple events in the same locus and widespread involvement of mobile elements in the gain and loss of defense genes. CONCLUSIONS: Evolution of microbial defense systems is highly dynamic but, notwithstanding the host-parasite arms race, generally follows the same trends that have been established for the rest of the genes. Apart from the paucity and the low flux of defense genes in parasitic bacteria with deteriorating genomes, there is no clear connection between the evolutionary regime of defense systems and microbial life style.