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\"The breakout hit of the biggest Spider-Event of the century is taking the comics world by storm with her own series! Gwen Stacy is Spider-Woman, but you knew that already. What you don't know is what friends and foes are waiting for her in the aftermath of Spider-Verse! From the fan-favorite creative team that brought you Spider-Gwen's origin story in Edge of Spider-Verse, Jason Latour and Robbie Rodriguez!\"--Provided from Amazon.com.

Biological nitrogen fixation is an important component of sustainable soil fertility and a key component of the nitrogen cycle. We used targeted metagenomics to study the nitrogen fixation-capable terrestrial bacterial community by targeting the gene for nitrogenase reductase ( nifH ). We obtained 1.1 million nifH 454 amplicon sequences from 222 soil samples collected from 4 National Ecological Observatory Network (NEON) sites in Alaska, Hawaii, Utah, and Florida. To accurately detect and correct frameshifts caused by indel sequencing errors, we developed FrameBot, a tool for frameshift correction and nearest-neighbor classification, and compared its accuracy to that of two other rapid frameshift correction tools. We found FrameBot was, in general, more accurate as long as a reference protein sequence with 80% or greater identity to a query was available, as was the case for virtually all nifH reads for the 4 NEON sites. Frameshifts were present in 12.7% of the reads. Those nifH sequences related to the Proteobacteria phylum were most abundant, followed by those for Cyanobacteria in the Alaska and Utah sites. Predominant genera with nifH sequences similar to reads included Azospirillum , Bradyrhizobium , and Rhizobium , the latter two without obvious plant hosts at the sites. Surprisingly, 80% of the sequences had greater than 95% amino acid identity to known nifH gene sequences. These samples were grouped by site and correlated with soil environmental factors, especially drainage, light intensity, mean annual temperature, and mean annual precipitation. FrameBot was tested successfully on three ecofunctional genes but should be applicable to any. IMPORTANCE High-throughput phylogenetic analysis of microbial communities using rRNA-targeted sequencing is now commonplace; however, such data often allow little inference with respect to either the presence or the diversity of genes involved in most important ecological processes. To study the gene pool for these processes, it is more straightforward to assess the genes directly responsible for the ecological function (ecofunctional genes). However, analyzing these genes involves technical challenges beyond those seen for rRNA. In particular, frameshift errors cause garbled downstream protein translations. Our FrameBot tool described here both corrects frameshift errors in query reads and determines their closest matching protein sequences in a set of reference sequences. We validated this new tool with sequences from defined communities and demonstrated the tool’s utility on nifH gene fragments sequenced from soils in well-characterized and major terrestrial ecosystem types. High-throughput phylogenetic analysis of microbial communities using rRNA-targeted sequencing is now commonplace; however, such data often allow little inference with respect to either the presence or the diversity of genes involved in most important ecological processes. To study the gene pool for these processes, it is more straightforward to assess the genes directly responsible for the ecological function (ecofunctional genes). However, analyzing these genes involves technical challenges beyond those seen for rRNA. In particular, frameshift errors cause garbled downstream protein translations. Our FrameBot tool described here both corrects frameshift errors in query reads and determines their closest matching protein sequences in a set of reference sequences. We validated this new tool with sequences from defined communities and demonstrated the tool’s utility on nifH gene fragments sequenced from soils in well-characterized and major terrestrial ecosystem types.

Sphingomonas wittichii strain RW1 (RW1) is one of the few strains that can grow on dibenzo-p-dioxin (DD). We conducted a transcriptomic study of RW1 using RNA-Seq to outline transcriptional responses to DD, dibenzofuran (DF), and the smectite clay mineral saponite with succinate as carbon source. The ability to grow on DD is rare compared to growth on the chemically similar DF even though the same initial dioxygenase may be involved in oxidation of both substrates. Therefore, we hypothesized the reason for this lies beyond catabolic pathways and may concern genes involved in processes for cell-substrate interactions such as substrate recognition, transport, and detoxification. Compared to succinate (SUC) as control carbon source, DF caused over 240 protein-coding genes to be differentially expressed, whereas more than 300 were differentially expressed with DD. Stress response genes were up-regulated in response to both DD and DF. This effect was stronger with DD than DF, suggesting a higher toxicity of DD compared to DF. Both DD and DF caused changes in expression of genes involved in active cross-membrane transport such as TonB-dependent receptor proteins, but the patterns of change differed between the two substrates. Multiple transcription factor genes also displayed expression patterns distinct to DD and DF growth. DD and DF induced the catechol ortho- and the salicylate/gentisate pathways, respectively. Both DD and DF induced the shared down-stream aliphatic intermediate compound pathway. Clay caused category-wide down-regulation of genes for cell motility and chemotaxis, particularly those involved in the synthesis, assembly and functioning of flagella. This is an environmentally important finding because clay is a major component of soil microbes' microenvironment influencing local chemistry and may serve as a geosorbent for toxic pollutants. Similar to clay, DD and DF also affected expression of genes involved in motility and chemotaxis.

Standing genetic variation and the historical environment in which that variation arises (evolutionary history) are both potentially significant determinants of a population's capacity for evolutionary response to a changing environment. Using the open-ended digital evolution software Avida, we evaluated the relative importance of these two factors in influencing evolutionary trajectories in the face of sudden environmental change. We examined how historical exposure to predation pressures, different levels of genetic variation, and combinations of the two, affected the evolvability of anti-predator strategies and competitive abilities in the presence or absence of threats from new, invasive predator populations. We show that while standing genetic variation plays some role in determining evolutionary responses, evolutionary history has the greater influence on a population's capacity to evolve anti-predator traits, i.e. traits effective against novel predators. This adaptability likely reflects the relative ease of repurposing existing, relevant genes and traits, and the broader potential value of the generation and maintenance of adaptively flexible traits in evolving populations.

High-throughput sequencing of 16S rRNA genes has increased our understanding of microbial community structure, but now even higher-throughput methods to the Illumina scale allow the creation of much larger datasets with more samples and orders-of-magnitude more sequences that swamp current analytic methods. We developed a method capable of handling these larger datasets on the basis of assignment of sequences into an existing taxonomy using a supervised learning approach (taxonomy-supervised analysis). We compared this method with a commonly used clustering approach based on sequence similarity (taxonomy-unsupervised analysis). We sampled 211 different bacterial communities from various habitats and obtained ∼1.3 million 16S rRNA sequences spanning the V4 hypervariable region by pyrosequencing. Both methodologies gave similar ecological conclusions in that β-diversity measures calculated by using these two types of matrices were significantly correlated to each other, as were the ordination configurations and hierarchical clustering dendrograms. In addition, our taxonomy-supervised analyses were also highly correlated with phylogenetic methods, such as UniFrac. The taxonomy-supervised analysis has the advantages that it is not limited by the exhaustive computation required for the alignment and clustering necessary for the taxonomy-unsupervised analysis, is more tolerant of sequencing errors, and allows comparisons when sequences are from different regions of the 16S rRNA gene. With the tremendous expansion in 16S rRNA data acquisition underway, the taxonomy-supervised approach offers the potential to provide more rapid and extensive community comparisons across habitats and samples.

Background Metagenomics can provide important insight into microbial communities. However, assembling metagenomic datasets has proven to be computationally challenging. Current methods often assemble only fragmented partial genes. Results We present a novel method for targeting assembly of specific protein-coding genes. This method combines a de Bruijn graph, as used in standard assembly approaches, and a protein profile hidden Markov model (HMM) for the gene of interest, as used in standard annotation approaches. These are used to create a novel combined weighted assembly graph. Xander performs both assembly and annotation concomitantly using information incorporated in this graph. We demonstrate the utility of this approach by assembling contigs for one phylogenetic marker gene and for two functional marker genes, first on Human Microbiome Project (HMP)-defined community Illumina data and then on 21 rhizosphere soil metagenomic datasets from three different crops totaling over 800 Gbp of unassembled data. We compared our method to a recently published bulk metagenome assembly method and a recently published gene-targeted assembler and found our method produced more, longer, and higher quality gene sequences. Conclusion Xander combines gene assignment with the rapid assembly of full-length or near full-length functional genes from metagenomic data without requiring bulk assembly or post-processing to find genes of interest. HMMs used for assembly can be tailored to the targeted genes, allowing flexibility to improve annotation over generic annotation pipelines. This method is implemented as open source software and is available at https://github.com/rdpstaff/Xander_assembler .

The khmer package is a freely available software library for working efficiently with fixed length DNA words, or k-mers. khmer provides implementations of a probabilistic k-mer counting data structure, a compressible De Bruijn graph representation, De Bruijn graph partitioning, and digital normalization. khmer is implemented in C++ and Python, and is freely available under the BSD license at  https://github.com/dib-lab/khmer/.

Standing genetic variation and the historical environment in which that variation arises (evolutionary history) are both potentially significant determinants of a population s capacity for evolutionary response to a changing environment. We evaluated the relative importance of these two factors in influencing the evolutionary trajectories in the face of sudden environmental change. We used the open-ended digital evolution software Avida to examine how historic exposure to predation pressures, different levels of genetic variation, and combinations of the two, impact anti-predator strategies and competitive abilities evolved in the face of threats from new, invasive, predator populations. We show that while standing genetic variation plays some role in determining evolutionary responses, evolutionary history has the greater influence on a population s capacity to evolve effective anti-predator traits. This adaptability likely reflects the relative ease of repurposing existing, relevant genes and traits, and the broader potential value of the generation and maintenance of adaptively flexible traits in evolving populations.

Activation of the steroidogenic enzyme CYP11A1 was shown to be necessary for the development of peanut-induced intestinal anaphylaxis and IL-13 production in allergic mice. We determined if levels of CYP11A1 in peripheral blood T cells from peanut-allergic (PA) children compared to non-allergic controls were increased and if levels correlated to IL-13 production and oral challenge outcomes to peanut. CYP11A1 mRNA and protein levels were significantly increased in activated CD4+ T cells from PA patients. In parallel, IL-13 production was significantly increased; IFNγ levels were not different between groups. There were significant correlations between expression levels of CYP11A1 mRNA and levels of IL13 mRNA and protein, levels of serum IgE anti-Ara h 2 and to outcomes of peanut challenge. The importance of CYP11A1 on cytokine production was tested using a CYP11A1 CRISPR/Cas9 KO plasmid or an inhibitor of enzymatic CYP11A1 activity. Inhibition of CYP11A1 activation in patient cells treated with the inhibitor, aminoglutethimide, or CD4+ T cell line transfected with the CYP11A1 KO plasmid resulted in reduced IL-13 production. These data suggest that the CYP11A1-CD4+Tcell-IL-13 axis in activated CD4+ T cells from PA children is associated with development of PA reactions. CYP11A1 may represent a novel target for therapeutic intervention in PA children.

Background In recent years public health research has shifted to more strengths or asset-based approaches to health research but there is little understanding of what this concept means to Indigenous researchers. Therefore our purpose was to define an Indigenous strengths-based approach to health and well-being research. Methods Using Group Concept Mapping, Indigenous health researchers ( N  = 27) participated in three-phases. Phase 1: Participants provided 218 unique responses to the focus prompt “Indigenous Strengths-Based Health and Wellness Research…” Redundancies and irrelevant statements were removed using content analysis, resulting in a final set of 94 statements. Phase 2: Participants sorted statements into groupings and named these groupings. Participants rated each statement based on importance using a 4-point scale. Hierarchical cluster analysis was used to create clusters based on how statements were grouped by participants. Phase 3: Two virtual meetings were held to share and invite researchers to collaboratively interpret results. Results A six-cluster map representing the meaning of Indigenous strengths-based health and wellness research was created. Results of mean rating analysis showed all six clusters were rated on average as moderately important. Conclusions The definition of Indigenous strengths-based health research, created through collaboration with leading AI/AN health researchers, centers Indigenous knowledges and cultures while shifting the research narrative from one of illness to one of flourishing and relationality. This framework offers actionable steps to researchers, public health practitioners, funders, and institutions to promote relational, strengths-based research that has the potential to promote Indigenous health and wellness at individual, family, community, and population levels.