Genomic Analysis of Aspergillus Section Terrei Reveals a High Potential in Secondary Metabolite Production and Plant Biomass Degradation.

Aspergillus terreus has attracted interest due to its application in industrial biotechnology, particularly for the production of itaconic acid and bioactive secondary metabolites. As related species also seem to possess a prosperous secondary metabolism, they are of high interest for genome mining and exploitation. Here, we present draft genome sequences for six species from Aspergillus section Terrei and one species from Aspergillus section Nidulantes. Whole-genome phylogeny confirmed that section Terrei is monophyletic. Genome analyses identified between 70 and 108 key secondary metabolism genes in each of the genomes of section Terrei, the highest rate found in the genus Aspergillus so far. The respective enzymes fall into 167 distinct families with most of them corresponding to potentially unique compounds or compound families. Moreover, 53% of the families were only found in a single species, which supports the suitability of species from section Terrei for further genome mining. Intriguingly, this analysis, combined with heterologous gene expression and metabolite identification, suggested that species from section Terrei use a strategy for UV protection different to other species from the genus Aspergillus. Section Terrei contains a complete plant polysaccharide degrading potential and an even higher cellulolytic potential than other Aspergilli, possibly facilitating additional applications for these species in biotechnology.

An integrated metagenomic, metabolomic and transcriptomic survey of Populus across genotypes and environments.

Bridging molecular information to ecosystem-level processes would provide the capacity to understand system vulnerability and, potentially, a means for assessing ecosystem health. Here, we present an integrated dataset containing environmental and metagenomic information from plant-associated microbial communities, plant transcriptomics, plant and soil metabolomics, and soil chemistry and activity characterization measurements derived from the model tree species Populus trichocarpa. Soil, rhizosphere, root endosphere, and leaf samples were collected from 27 different P. trichocarpa genotypes grown in two different environments leading to an integrated dataset of 318 metagenomes, 98 plant transcriptomes, and 314 metabolomic profiles that are supported by diverse soil measurements. This expansive dataset will provide insights into causal linkages that relate genomic features and molecular level events to system-level properties and their environmental influences.

Whole community shotgun metagenomes of two biological soil crust types from the Mojave Desert.

We present six whole community shotgun metagenomic sequencing data sets of two types of biological soil crusts sampled at the ecotone of the Mojave Desert and Colorado Desert in California. These data will help us understand the diversity and function of biocrust microbial communities, which are essential for desert ecosystems.

Assessment of Autonomic Nervous System Activity Using Spectral Analysis of Heart Rate Variability After Continuous Positive Airway Pressure (CPAP) Therapy in Patients With Sleep Apnea.

Heart rate variability (HRV) measurements have emerged as a valuable tool for understanding the functioning of the autonomic nervous system (ANS) and assessing the health outcomes of obstructive sleep apnea (OSA) in patients. Sleep and the ANS exert a mutual influence on each other. Sleep promotes relaxation and recovery of the ANS. Conversely, ANS activity plays a role in regulating the onset and maintenance of sleep. The impact of continuous positive airway pressure (CPAP) therapy on patient recovery levels was investigated by assessing the restoration of ANS activity using HRV indicators. The study included patients with OSA who had been on CPAP for at least eight weeks. The patients were divided into two groups, namely the experimental group (CPAP-compliant) and the control group (CPAP-non-compliant). The study included a total of 38 patients, with 20 in the CPAP-compliant group and 18 in the CPAP-non-compliant group. The HRV analysis included time- and frequency-domain measures. Data was collected in various resting conditions, including lying down, standing, regular breathing, and under physiological stress induced by deep breathing and the Valsalva maneuver. After CPAP treatment, there was an increase in the average values for SDNN for deep breathing and Valsalva maneuvers. The mean changes in SDNN for CPAP-non-compliant versus CPAP-compliant groups for normal breathing increased from 32.50±5.33 to 42.40±8.03, while the values for Valsalva increased from 20.16±2.47 to 25.45±3.03. Despite the observed variations in SDNN, there was no significant change in the average change in heart rate (∆ HR), except during the Valsalva maneuver. Post-CPAP values for the Valsalva ratio were significantly decreased in deep breathing. The E:I ratio for the CPAP-compliant group during normal breathing was 1.08±.16 compared to 1.55±.09; t (36) =-11.15, p <0.001 in the CPAP-non-compliant group. During deep breathing, the ratio was 1.36±.15 versus 1.59±.24; t (36) =-3.578, p <0.001. The high frequency (HF)nu mean values for deep breathing were 34.06±5.546 compared to 35.00±6.358; t (36) = -.485, p=.630. For the Valsalva maneuver, the values were 29.94±4.721 versus 26.95±6.621; t (36) =1.589, p=.060. The HF/low frequency (LF) ratio was found to be significant only in supine, standing, and normal breathing. The utilization of CPAP therapy was found to be effective in achieving and sustaining autonomic balance during tasks like standing and engaging in regular breathing patterns. During activities that involve intense physical effort, like the Valsalva maneuver, the HRV metrics did not indicate any significant balance between sympathetic and parasympathetic activity. However, using CPAP therapy for a prolonged period can be beneficial in consistently improving the sympathovagal balance in these patients.

Metatranscriptomes of two biological soil crust types from the Mojave desert in response to wetting.

We present eight metatranscriptomic datasets of light algal and cyanolichen biological soil crusts from the Mojave Desert in response to wetting. These data will help us understand gene expression patterns in desert biocrust microbial communities after they have been reactivated by the addition of water.

Metatranscriptomics sheds light on the links between the functional traits of fungal guilds and ecological processes in forest soil ecosystems.

Soil fungi belonging to different functional guilds, such as saprotrophs, pathogens, and mycorrhizal symbionts, play key roles in forest ecosystems. To date, no study has compared the actual gene expression of these guilds in different forest soils. We used metatranscriptomics to study the competition for organic resources by these fungal groups in boreal, temperate, and Mediterranean forest soils. Using a dedicated mRNA annotation pipeline combined with the JGI MycoCosm database, we compared the transcripts of these three fungal guilds, targeting enzymes involved in C- and N mobilization from plant and microbial cell walls. Genes encoding enzymes involved in the degradation of plant cell walls were expressed at a higher level in saprotrophic fungi than in ectomycorrhizal and pathogenic fungi. However, ectomycorrhizal and saprotrophic fungi showed similarly high expression levels of genes encoding enzymes involved in fungal cell wall degradation. Transcripts for N-related transporters were more highly expressed in ectomycorrhizal fungi than in other groups. We showed that ectomycorrhizal and saprotrophic fungi compete for N in soil organic matter, suggesting that their interactions could decelerate C cycling. Metatranscriptomics provides a unique tool to test controversial ecological hypotheses and to better understand the underlying ecological processes involved in soil functioning and carbon stabilization.

Genome-scale phylogeny and comparative genomics of the fungal order Sordariales.

The order Sordariales is taxonomically diverse, and harbours many species with different lifestyles and large economic importance. Despite its importance, a robust genome-scale phylogeny, and associated comparative genomic analysis of the order is lacking. In this study, we examined whole-genome data from 99 Sordariales, including 52 newly sequenced genomes, and seven outgroup taxa. We inferred a comprehensive phylogeny that resolved several contentious relationships amongst families in the order, and cleared-up intrafamily relationships within the Podosporaceae. Extensive comparative genomics showed that genomes from the three largest families in the dataset (Chaetomiaceae, Podosporaceae and Sordariaceae) differ greatly in GC content, genome size, gene number, repeat percentage, evolutionary rate, and genome content affected by repeat-induced point mutations (RIP). All genomic traits showed phylogenetic signal, and ancestral state reconstruction revealed that the variation of the properties stems primarily from within-family evolution. Together, the results provide a thorough framework for understanding genome evolution in this important group of fungi.

A standardized quantitative analysis strategy for stable isotope probing metagenomics.

Stable isotope probing (SIP) facilitates culture-independent identification of active microbial populations within complex ecosystems through isotopic enrichment of nucleic acids. Many DNA-SIP studies rely on 16S rRNA gene sequences to identify active taxa, but connecting these sequences to specific bacterial genomes is often challenging. Here, we describe a standardized laboratory and analysis framework to quantify isotopic enrichment on a per-genome basis using shotgun metagenomics instead of 16S rRNA gene sequencing. To develop this framework, we explored various sample processing and analysis approaches using a designed microbiome where the identity of labeled genomes and their level of isotopic enrichment were experimentally controlled. With this ground truth dataset, we empirically assessed the accuracy of different analytical models for identifying active taxa and examined how sequencing depth impacts the detection of isotopically labeled genomes. We also demonstrate that using synthetic DNA internal standards to measure absolute genome abundances in SIP density fractions improves estimates of isotopic enrichment. In addition, our study illustrates the utility of internal standards to reveal anomalies in sample handling that could negatively impact SIP metagenomic analyses if left undetected. Finally, we present SIPmg, an R package to facilitate the estimation of absolute abundances and perform statistical analyses for identifying labeled genomes within SIP metagenomic data. This experimentally validated analysis framework strengthens the foundation of DNA-SIP metagenomics as a tool for accurately measuring the in situ activity of environmental microbial populations and assessing their genomic potential. IMPORTANCE Answering the questions, "who is eating what?" and "who is active?" within complex microbial communities is paramount for our ability to model, predict, and modulate microbiomes for improved human and planetary health. These questions can be pursued using stable isotope probing to track the incorporation of labeled compounds into cellular DNA during microbial growth. However, with traditional stable isotope methods, it is challenging to establish links between an active microorganism's taxonomic identity and genome composition while providing quantitative estimates of the microorganism's isotope incorporation rate. Here, we report an experimental and analytical workflow that lays the foundation for improved detection of metabolically active microorganisms and better quantitative estimates of genome-resolved isotope incorporation, which can be used to further refine ecosystem-scale models for carbon and nutrient fluxes within microbiomes.

Terabase-Scale Coassembly of a Tropical Soil Microbiome.

Petabases of environmental metagenomic data are publicly available, presenting an opportunity to characterize complex environments and discover novel lineages of life. Metagenome coassembly, in which many metagenomic samples from an environment are simultaneously analyzed to infer the underlying genomes' sequences, is an essential tool for achieving this goal. We applied MetaHipMer2, a distributed metagenome assembler that runs on supercomputing clusters, to coassemble 3.4 terabases (Tbp) of metagenome data from a tropical soil in the Luquillo Experimental Forest (LEF), Puerto Rico. The resulting coassembly yielded 39 high-quality (>90% complete, <5% contaminated, with predicted 23S, 16S, and 5S rRNA genes and ≥18 tRNAs) metagenome-assembled genomes (MAGs), including two from the candidate phylum Eremiobacterota. Another 268 medium-quality (≥50% complete, <10% contaminated) MAGs were extracted, including the candidate phyla Dependentiae, Dormibacterota, and Methylomirabilota. In total, 307 medium- or higher-quality MAGs were assigned to 23 phyla, compared to 294 MAGs assigned to nine phyla in the same samples individually assembled. The low-quality (<50% complete, <10% contaminated) MAGs from the coassembly revealed a 49% complete rare biosphere microbe from the candidate phylum FCPU426 among other low-abundance microbes, an 81% complete fungal genome from the phylum Ascomycota, and 30 partial eukaryotic MAGs with ≥10% completeness, possibly representing protist lineages. A total of 22,254 viruses, many of them low abundance, were identified. Estimation of metagenome coverage and diversity indicates that we may have characterized ≥87.5% of the sequence diversity in this humid tropical soil and indicates the value of future terabase-scale sequencing and coassembly of complex environments. IMPORTANCE Petabases of reads are being produced by environmental metagenome sequencing. An essential step in analyzing these data is metagenome assembly, the computational reconstruction of genome sequences from microbial communities. "Coassembly" of metagenomic sequence data, in which multiple samples are assembled together, enables more complete detection of microbial genomes in an environment than "multiassembly," in which samples are assembled individually. To demonstrate the potential for coassembling terabases of metagenome data to drive biological discovery, we applied MetaHipMer2, a distributed metagenome assembler that runs on supercomputing clusters, to coassemble 3.4 Tbp of reads from a humid tropical soil environment. The resulting coassembly, its functional annotation, and analysis are presented here. The coassembly yielded more, and phylogenetically more diverse, microbial, eukaryotic, and viral genomes than the multiassembly of the same data. Our resource may facilitate the discovery of novel microbial biology in tropical soils and demonstrates the value of terabase-scale metagenome sequencing.

Multiomics in the central Arctic Ocean for benchmarking biodiversity change.

Multiomics approaches need to be applied in the central Arctic Ocean to benchmark biodiversity change and to identify novel species and their genes. As part of MOSAiC, EcoOmics will therefore be essential for conservation and sustainable bioprospecting in one of the least explored ecosystems on Earth.

Draft Metagenome Sequences of the Sphagnum (Peat Moss) Microbiome from Ambient and Warmed Environments across Europe.

We present 49 metagenome assemblies of the microbiome associated with Sphagnum (peat moss) collected from ambient, artificially warmed, and geothermally warmed conditions across Europe. These data will enable further research regarding the impact of climate change on plant-microbe symbiosis, ecology, and ecosystem functioning of northern peatland ecosystems.

Sodalis ligni Strain 159R Isolated from an Anaerobic Lignin-Degrading Consortium.

Novel bacterial isolates with the capabilities of lignin depolymerization, catabolism, or both, could be pertinent to lignocellulosic biofuel applications. In this study, we aimed to identify anaerobic bacteria that could address the economic challenges faced with microbial-mediated biotechnologies, such as the need for aeration and mixing. Using a consortium seeded from temperate forest soil and enriched under anoxic conditions with organosolv lignin as the sole carbon source, we successfully isolated a novel bacterium, designated 159R. Based on the 16S rRNA gene, the isolate belongs to the genus Sodalis in the family Bruguierivoracaceae. Whole-genome sequencing revealed a genome size of 6.38 Mbp and a GC content of 55 mol%. To resolve the phylogenetic position of 159R, its phylogeny was reconstructed using (i) 16S rRNA genes of its closest relatives, (ii) multilocus sequence analysis (MLSA) of 100 genes, (iii) 49 clusters of orthologous groups (COG) domains, and (iv) 400 conserved proteins. Isolate 159R was closely related to the deadwood associated Sodalis guild rather than the tsetse fly and other insect endosymbiont guilds. Estimated genome-sequence-based digital DNA-DNA hybridization (dDDH), genome percentage of conserved proteins (POCP), and an alignment analysis between 159R and the Sodalis clade species further supported that isolate 159R was part of the Sodalis genus and a strain of Sodalis ligni. We proposed the name Sodalis ligni str. 159R (=DSM 110549 = ATCC TSD-177). IMPORTANCE Currently, in the paper industry, paper mill pulping relies on unsustainable and costly processes to remove lignin from lignocellulosic material. A greener approach is biopulping, which uses microbes and their enzymes to break down lignin. However, there are limitations to biopulping that prevent it from outcompeting other pulping processes, such as requiring constant aeration and mixing. Anaerobic bacteria are a promising alternative source for consolidated depolymerization of lignin and its conversion to valuable by-products. We presented Sodalis ligni str. 159R and its characteristics as another example of potential mechanisms that can be developed for lignocellulosic applications.

Comparative genomics reveals a dynamic genome evolution in the ectomycorrhizal milk-cap (Lactarius) mushrooms.

Ectomycorrhizal fungi play a key role in forests by establishing mutualistic symbioses with woody plants. Genome analyses have identified conserved symbiosis-related traits among ectomycorrhizal fungal species, but the molecular mechanisms underlying host specificity remain poorly known. We sequenced and compared the genomes of seven species of milk-cap fungi (Lactarius, Russulales) with contrasting host specificity. We also compared these genomes with those of symbiotic and saprotrophic Russulales species, aiming to identify genes involved in their ecology and host specificity. The size of Lactarius genomes is significantly larger than other Russulales species, owing to a massive accumulation of transposable elements and duplication of dispensable genes. As expected, their repertoire of genes coding for plant cell wall-degrading enzymes is restricted, but they retained a substantial set of genes involved in microbial cell wall degradation. Notably, Lactarius species showed a striking expansion of genes encoding proteases, such as secreted ectomycorrhiza-induced sedolisins. A high copy number of genes coding for small secreted LysM proteins and Lactarius-specific lectins were detected, which may be linked to host specificity. This study revealed a large diversity in the genome landscapes and gene repertoires within Russulaceae. The known host specificity of Lactarius symbionts may be related to mycorrhiza-induced species-specific genes, including secreted sedolisins.

Deeplasmid: deep learning accurately separates plasmids from bacterial chromosomes.

Plasmids are mobile genetic elements that play a key role in microbial ecology and evolution by mediating horizontal transfer of important genes, such as antimicrobial resistance genes. Many microbial genomes have been sequenced by short read sequencers and have resulted in a mix of contigs that derive from plasmids or chromosomes. New tools that accurately identify plasmids are needed to elucidate new plasmid-borne genes of high biological importance. We have developed Deeplasmid, a deep learning tool for distinguishing plasmids from bacterial chromosomes based on the DNA sequence and its encoded biological data. It requires as input only assembled sequences generated by any sequencing platform and assembly algorithm and its runtime scales linearly with the number of assembled sequences. Deeplasmid achieves an AUC-ROC of over 89%, and it was more accurate than five other plasmid classification methods. Finally, as a proof of concept, we used Deeplasmid to predict new plasmids in the fish pathogen Yersinia ruckeri ATCC 29473 that has no annotated plasmids. Deeplasmid predicted with high reliability that a long assembled contig is part of a plasmid. Using long read sequencing we indeed validated the existence of a 102 kb long plasmid, demonstrating Deeplasmid's ability to detect novel plasmids.

Expanding the genomic encyclopedia of Actinobacteria with 824 isolate reference genomes.

The phylum Actinobacteria includes important human pathogens like Mycobacterium tuberculosis and Corynebacterium diphtheriae and renowned producers of secondary metabolites of commercial interest, yet only a small part of its diversity is represented by sequenced genomes. Here, we present 824 actinobacterial isolate genomes in the context of a phylum-wide analysis of 6,700 genomes including public isolates and metagenome-assembled genomes (MAGs). We estimate that only 30%-50% of projected actinobacterial phylogenetic diversity possesses genomic representation via isolates and MAGs. A comparison of gene functions reveals novel determinants of host-microbe interaction as well as environment-specific adaptations such as potential antimicrobial peptides. We identify plasmids and prophages across isolates and uncover extensive prophage diversity structured mainly by host taxonomy. Analysis of >80,000 biosynthetic gene clusters reveals that horizontal gene transfer and gene loss shape secondary metabolite repertoire across taxa. Our observations illustrate the essential role of and need for high-quality isolate genome sequences.

Genetic determinants of endophytism in the Arabidopsis root mycobiome.

The roots of Arabidopsis thaliana host diverse fungal communities that affect plant health and disease states. Here, we sequence the genomes of 41 fungal isolates representative of the A. thaliana root mycobiota for comparative analysis with other 79 plant-associated fungi. Our analyses indicate that root mycobiota members evolved from ancestors with diverse lifestyles and retain large repertoires of plant cell wall-degrading enzymes (PCWDEs) and effector-like small secreted proteins. We identify a set of 84 gene families associated with endophytism, including genes encoding PCWDEs acting on xylan (family GH10) and cellulose (family AA9). Transcripts encoding these enzymes are also part of a conserved transcriptional program activated by phylogenetically-distant mycobiota members upon host contact. Recolonization experiments with individual fungi indicate that strains with detrimental effects in mono-association with the host colonize roots more aggressively than those with beneficial activities, and dominate in natural root samples. Furthermore, we show that the pectin-degrading enzyme family PL1_7 links aggressiveness of endophytic colonization to plant health.

Long-read metagenomics of soil communities reveals phylum-specific secondary metabolite dynamics.

Microbial biosynthetic gene clusters (BGCs) encoding secondary metabolites are thought to impact a plethora of biologically mediated environmental processes, yet their discovery and functional characterization in natural microbiomes remains challenging. Here we describe deep long-read sequencing and assembly of metagenomes from biological soil crusts, a group of soil communities that are rich in BGCs. Taking advantage of the unusually long assemblies produced by this approach, we recovered nearly 3,000 BGCs for analysis, including 712 full-length BGCs. Functional exploration through metatranscriptome analysis of a 3-day wetting experiment uncovered phylum-specific BGC expression upon activation from dormancy, elucidating distinct roles and complex phylogenetic and temporal dynamics in wetting processes. For example, a pronounced increase in BGC transcription occurs at night primarily in cyanobacteria, implicating BGCs in nutrient scavenging roles and niche competition. Taken together, our results demonstrate that long-read metagenomic sequencing combined with metatranscriptomic analysis provides a direct view into the functional dynamics of BGCs in environmental processes and suggests a central role of secondary metabolites in maintaining phylogenetically conserved niches within biocrusts.

The biogeographic differentiation of algal microbiomes in the upper ocean from pole to pole.

Eukaryotic phytoplankton are responsible for at least 20% of annual global carbon fixation. Their diversity and activity are shaped by interactions with prokaryotes as part of complex microbiomes. Although differences in their local species diversity have been estimated, we still have a limited understanding of environmental conditions responsible for compositional differences between local species communities on a large scale from pole to pole. Here, we show, based on pole-to-pole phytoplankton metatranscriptomes and microbial rDNA sequencing, that environmental differences between polar and non-polar upper oceans most strongly impact the large-scale spatial pattern of biodiversity and gene activity in algal microbiomes. The geographic differentiation of co-occurring microbes in algal microbiomes can be well explained by the latitudinal temperature gradient and associated break points in their beta diversity, with an average breakpoint at 14 °C ± 4.3, separating cold and warm upper oceans. As global warming impacts upper ocean temperatures, we project that break points of beta diversity move markedly pole-wards. Hence, abrupt regime shifts in algal microbiomes could be caused by anthropogenic climate change.

Metagenome Sequencing to Explore Phylogenomics of Terrestrial Cyanobacteria.

Cyanobacteria are ubiquitous microorganisms with crucial ecosystem functions, yet most knowledge of their biology relates to aquatic taxa. We have constructed metagenomes for 50 taxonomically well-characterized terrestrial cyanobacterial cultures. These data will support phylogenomic studies of evolutionary relationships and gene content among these unique algae and their aquatic relatives.

Effects of external low intensity focused ultrasound on inflammatory markers in neuropathic pain.

BACKGROUND: Changes in inflammatory cytokine levels contribute to the induction and maintenance of neuropathic pain. We have shown that external low intensity focused ultrasound (liFUS) reduces allodynia in a common peroneal nerve injury (CPNI). Here, we investigate an underlying mechanism of action for this treatment and measure the effect of liFUS on inflammatory markers. METHODS: Male rats were divided into four groups: CPNI/liFUS, CPNI/shamliFUS, shamCPNI/liFUS, and shamCPNI/shamliFUS. Mechanical nociceptive thresholds were measured using Von Frey filaments (VFF) to confirm the absence/presence of allodynia at baseline, after CPNI, and after liFUS. Commercial microarray and ELISA assays were used to assess cytokine expression in the treated L5 dorsal root ganglion (DRG) and dorsal horn (DH) tissue 24 and 72 h after liFUS. RESULTS: VFF thresholds were significantly reduced following CPNI in both groups that received the injury (p < 0.001). After liFUS, only the CPNI/liFUS cohort showed a significant increase in mechanical thresholds (p < 0.001). CPNI significantly increased TNFa, IL6, CNTF, IL1b (p < 0.05 for all) levels in the DRG and DH, compared to baseline, consistent with previous work in sciatic nerve injury. LiFUS in CPNI rats resulted in a decrease in these cytokines in DRG 72 h post-therapy (TNFa, IL6, CNTF and IL1b, p < 0.001). In the DH, IL1b, CNTF, and TNFa (p < 0.05 for all) decreased 72 h after liFUS. CONCLUSION: We have demonstrated that liFUS modifies inflammatory cytokines in both DRG and DH in CPNI rats. These data provide evidence that liFUS, reverses the allodynic phenotype, in part, by altering inflammatory cytokine pathways.