Time-Resolved Multiomics Illustrates Host and Gut Microbe Interactions during Salmonella Infection.

Salmonella infection, also known as Salmonellosis, is one of the most common food-borne illnesses. Salmonella infection can trigger host defensive functions, including an inflammatory response. The provoked-host inflammatory response has a significant impact on the bacterial population in the gut. In addition, Salmonella competes with other gut microorganisms for survival and growth within the host. Compositional and functional alterations in gut bacteria occur because of the host immunological response and competition between Salmonella and the gut microbiome. Host variation and the inherent complexity of the gut microbial community make understanding commensal and pathogen interactions particularly difficult during a Salmonella infection. Here, we present metabolomics and lipidomics analyses along with the 16S rRNA sequence analysis, revealing a comprehensive view of the metabolic interactions between the host and gut microbiota during Salmonella infection in a CBA/J mouse model. We found that different metabolic pathways were altered over the four investigated time points of Salmonella infection (days -2, +2, +6, and +13). Furthermore, metatranscriptomics analysis integrated with metabolomics and lipidomics analysis facilitated an understanding of the heterogeneous response of mice, depending on the degree of dysbiosis.

kb_DRAM: annotation and metabolic profiling of genomes with DRAM in KBase.

Microbial genome annotation is the process of identifying structural and functional elements in DNA sequences and subsequently attaching biological information to those elements. DRAM is a tool developed to annotate bacterial, archaeal, and viral genomes derived from pure cultures or metagenomes. DRAM goes beyond traditional annotation tools by distilling multiple gene annotations to genome level summaries of functional potential. Despite these benefits, a downside of DRAM is the requirement of large computational resources, which limits its accessibility. Further, it did not integrate with downstream metabolic modeling tools that require genome annotation. To alleviate these constraints, DRAM and the viral counterpart, DRAM-v, are now available and integrated with the freely accessible KBase cyberinfrastructure. With kb_DRAM users can generate DRAM annotations and functional summaries from microbial or viral genomes in a point-and-click interface, as well as generate genome-scale metabolic models from DRAM annotations. AVAILABILITY AND IMPLEMENTATION: For kb_DRAM users, the kb_DRAM apps on KBase can be found in the catalog at https://narrative.kbase.us/#catalog/modules/kb_DRAM. For kb_DRAM users, a tutorial workflow with all documentation is available at https://narrative.kbase.us/narrative/129480. For kb_DRAM developers, software is available at https://github.com/shafferm/kb_DRAM.

ORT: a workflow linking genome-scale metabolic models with reactive transport codes.

MOTIVATION: Nutrient and contaminant behavior in the subsurface are governed by multiple coupled hydrobiogeochemical processes which occur across different temporal and spatial scales. Accurate description of macroscopic system behavior requires accounting for the effects of microscopic and especially microbial processes. Microbial processes mediate precipitation and dissolution and change aqueous geochemistry, all of which impacts macroscopic system behavior. As 'omics data describing microbial processes is increasingly affordable and available, novel methods for using this data quickly and effectively for improved ecosystem models are needed. RESULTS: We propose a workflow ('Omics to Reactive Transport-ORT) for utilizing metagenomic and environmental data to describe the effect of microbiological processes in macroscopic reactive transport models. This workflow utilizes and couples two open-source software packages: KBase (a software platform for systems biology) and PFLOTRAN (a reactive transport modeling code). We describe the architecture of ORT and demonstrate an implementation using metagenomic and geochemical data from a river system. Our demonstration uses microbiological drivers of nitrification and denitrification to predict nitrogen cycling patterns which agree with those provided with generalized stoichiometries. While our example uses data from a single measurement, our workflow can be applied to spatiotemporal metagenomic datasets to allow for iterative coupling between KBase and PFLOTRAN. AVAILABILITY AND IMPLEMENTATION: Interactive models available at https://pflotranmodeling.paf.subsurfaceinsights.com/pflotran-simple-model/. Microbiological data available at NCBI via BioProject ID PRJNA576070. ORT Python code available at https://github.com/subsurfaceinsights/ort-kbase-to-pflotran. KBase narrative available at https://narrative.kbase.us/narrative/71260 or static narrative (no login required) at https://kbase.us/n/71260/258. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Optimization of proteomics sample preparation for identification of host and bacterial proteins in mouse feces.

Bottom-up proteomics is a powerful method for the functional characterization of mouse gut microbiota. To date, most of the bottom-up proteomics studies of the mouse gut rely on limited amounts of fecal samples. With mass-limited samples, the performance of such analyses is highly dependent on the protein extraction protocols and contaminant removal strategies. Here, protein extraction protocols (using different lysis buffers) and contaminant removal strategies (using different types of filters and beads) were systematically evaluated to maximize quantitative reproducibility and the number of identified proteins. Overall, our results recommend a protein extraction method using a combination of sodium dodecyl sulfate (SDS) and urea in Tris-HCl to yield the greatest number of protein identifications. These conditions led to an increase in the number of proteins identified from gram-positive bacteria, such as Firmicutes and Actinobacteria, which is a challenging task. Our analysis further confirmed these conditions led to the extraction of non-abundant bacterial phyla such as Proteobacteria. In addition, we found that, when coupled to our optimized extraction method, suspension trap (S-Trap) outperforms other contaminant removal methods by providing the most reproducible method while producing the greatest number of protein identifications. Overall, our optimized sample preparation workflow is straightforward and fast, and requires minimal sample handling. Furthermore, our approach does not require high amounts of fecal samples, a vital consideration in proteomics studies where mice produce smaller amounts of feces due to a particular physiological condition. Our final method provides efficient digestion of mouse fecal material, is reproducible, and leads to high proteomic coverage for both host and microbiome proteins.

DRAM for distilling microbial metabolism to automate the curation of microbiome function.

Microbial and viral communities transform the chemistry of Earth's ecosystems, yet the specific reactions catalyzed by these biological engines are hard to decode due to the absence of a scalable, metabolically resolved, annotation software. Here, we present DRAM (Distilled and Refined Annotation of Metabolism), a framework to translate the deluge of microbiome-based genomic information into a catalog of microbial traits. To demonstrate the applicability of DRAM across metabolically diverse genomes, we evaluated DRAM performance on a defined, in silico soil community and previously published human gut metagenomes. We show that DRAM accurately assigned microbial contributions to geochemical cycles and automated the partitioning of gut microbial carbohydrate metabolism at substrate levels. DRAM-v, the viral mode of DRAM, established rules to identify virally-encoded auxiliary metabolic genes (AMGs), resulting in the metabolic categorization of thousands of putative AMGs from soils and guts. Together DRAM and DRAM-v provide critical metabolic profiling capabilities that decipher mechanisms underpinning microbiome function.

Gut microbiota from high-risk men who have sex with men drive immune activation in gnotobiotic mice and in vitro HIV infection.

Men who have sex with men (MSM) have differences in immune activation and gut microbiome composition compared with men who have sex with women (MSW), even in the absence of HIV infection. Gut microbiome differences associated with HIV itself when controlling for MSM, as assessed by 16S rRNA sequencing, are relatively subtle. Understanding whether gut microbiome composition impacts immune activation in HIV-negative and HIV-positive MSM has important implications since immune activation has been associated with HIV acquisition risk and disease progression. To investigate the effects of MSM and HIV-associated gut microbiota on immune activation, we transplanted feces from HIV-negative MSW, HIV-negative MSM, and HIV-positive untreated MSM to gnotobiotic mice. Following transplant, 16S rRNA gene sequencing determined that the microbiomes of MSM and MSW maintained distinct compositions in mice and that specific microbial differences between MSM and MSW were replicated. Immunologically, HIV-negative MSM donors had higher frequencies of blood CD38+ HLADR+ and CD103+ T cells and their fecal recipients had higher frequencies of gut CD69+ and CD103+ T cells, compared with HIV-negative MSW donors and recipients, respectively. Significant microbiome differences were not detected between HIV-negative and HIV-positive MSM in this small donor cohort, and immune differences between their recipients were trending but not statistically significant. A larger donor cohort may therefore be needed to detect immune-modulating microbes associated with HIV. To investigate whether our findings in mice could have implications for HIV replication, we infected primary human lamina propria cells stimulated with isolated fecal microbiota, and found that microbiota from MSM stimulated higher frequencies of HIV-infected cells than microbiota from MSW. Finally, we identified several microbes that correlated with immune readouts in both fecal recipients and donors, and with in vitro HIV infection, which suggests a role for gut microbiota in immune activation and potentially HIV acquisition in MSM.

A natural supplement formula reduces anti-oxidative stress and enhances osteo-chondrogenic differentiation potential in mesenchymal stem cells.

There is great interest in using natural supplements to treat various medical conditions. In this study, we evaluated the anti-oxidative and stem cell differentiation effects of a mixture of vitamin D, Lactobacillus rhamnosus, ginger, curcumin, and Boswellia extract. The calcein acetoxymethyl assay after H2O2 treatment showed that combined natural supplement had an anti-oxidative effect. NS-J also increased calcium deposition, as shown by Alizarin Red S staining, indicating bone formation activity. The contents of type II collagen and glycosaminoglycans, which are biomarkers of cartilage, were higher in mesenchymal stem cells treated with combined natural supplement than in cells treated with individual ingredients of the formula. In mesenchymal stem cells treated with human osteoarthritis synovial fluids, combined natural supplement enhanced the expression of type II collagen and PPAR-δ, overcoming the anti-chondrogenic effect of inflammatory conditions. Combined natural supplement also inhibited Oil Red O staining in cells, which indicates inhibited adipogenesis. Thus, combined natural supplement, a formula comprising vitamin D, Lactobacillus rhamnosus, ginger, curcumin and Boswellia extract, reduced oxidative stress, enhanced osteogenesis and chondrogenesis, and inhibited adipogenesis in mesenchymal stem cells to a greater extent than the individual ingredients, indicating synergistic interaction. In addition, combined natural supplement increased the expression PPAR-δ, suggesting that these effects correlate with the PPAR-δ pathway.

Differences in fecal microbial metabolites and microbiota of children with autism spectrum disorders.

Evidence supporting that gut problems are linked to ASD symptoms has been accumulating both in humans and animal models of ASD. Gut microbes and their metabolites may be linked not only to GI problems but also to ASD behavior symptoms. Despite this high interest, most previous studies have looked mainly at microbial structure, and studies on fecal metabolites are rare in the context of ASD. Thus, we aimed to detect fecal metabolites that may be present at significantly different concentrations between 21 children with ASD and 23 neurotypical children and to investigate its possible link to human gut microbiome. Using 1H-NMR spectroscopy and 16S rRNA gene amplicon sequencing, we examined metabolite profiles and microbial compositions in fecal samples, respectively. Of the 59 metabolites detected, isopropanol concentrations were significantly higher in feces of children with ASD after multiple testing corrections. We also observed similar trends of fecal metabolites to previous studies; children with ASD have higher fecal p-cresol and possibly lower GABA concentrations. In addition, Fisher Discriminant Analysis (FDA) with leave-out-validation suggested that a group of metabolites-caprate, nicotinate, glutamine, thymine, and aspartate-may potentially function as a modest biomarker to separate ASD participants from the neurotypical group (78% sensitivity and 81% specificity). Consistent with our previous Arizona cohort study, we also confirmed lower gut microbial diversity and reduced relative abundances of phylotypes most closely related to Prevotella copri in children with ASD. After multiple testing corrections, we also learned that relative abundances of Feacalibacterium prausnitzii and Haemophilus parainfluenzae were lower in feces of children with ASD. Despite a relatively short list of fecal metabolites, the data in this study support that children with ASD have altered metabolite profiles in feces when compared with neurotypical children and warrant further investigation of metabolites in larger cohorts.

Examination of potassium diode pumped alkali laser using He, Ar, CH4 and C2H6 as buffer gas.

We examined the performance of a potassium diode pumped alkali laser (K DPAL) using He, Ar, CH4, C2H6 and a mixture of He and CH4 as a buffer gas to provide spin-orbit mixing of the 4P3/2 and 4P1/2 states of Potassium atoms. We found that pure helium cannot be used as an efficient buffer gas for continuous wave lasing without using a flowing system with a considerable flow speed of about 100 m/s. In contrast, using a small amount of methane (10-20 Torr) mixed with helium, continuous wave lasing can be achieved using very moderate flow speeds of about 1 m/s.

Optical dosimetry probes to validate Monte Carlo and empirical-method-based NIR dose planning in the brain: publisher's note.

This note points out additional funding information that was not added to [Appl. Opt.55, 9875 (2016)APOPAI0003-693510.1364/AO.55.009875] during production.

Optical dosimetry probes to validate Monte Carlo and empirical-method-based NIR dose planning in the brain.

A three-dimensional photon dosimetry in tissues is critical in designing optical therapeutic protocols to trigger light-activated drug release. The objective of this study is to investigate the feasibility of a Monte Carlo-based optical therapy planning software by developing dosimetry tools to characterize and cross-validate the local photon fluence in brain tissue, as part of a long-term strategy to quantify the effects of photoactivated drug release in brain tumors. An existing GPU-based 3D Monte Carlo (MC) code was modified to simulate near-infrared photon transport with differing laser beam profiles within phantoms of skull bone (B), white matter (WM), and gray matter (GM). A novel titanium-based optical dosimetry probe with isotropic acceptance was used to validate the local photon fluence, and an empirical model of photon transport was developed to significantly decrease execution time for clinical application. Comparisons between the MC and the dosimetry probe measurements were on an average 11.27%, 13.25%, and 11.81% along the illumination beam axis, and 9.4%, 12.06%, 8.91% perpendicular to the beam axis for WM, GM, and B phantoms, respectively. For a heterogeneous head phantom, the measured % errors were 17.71% and 18.04% along and perpendicular to beam axis. The empirical algorithm was validated by probe measurements and matched the MC results (R2>0.99), with average % error of 10.1%, 45.2%, and 22.1% relative to probe measurements, and 22.6%, 35.8%, and 21.9% relative to the MC, for WM, GM, and B phantoms, respectively. The simulation time for the empirical model was 6 s versus 8 h for the GPU-based Monte Carlo for a head phantom simulation. These tools provide the capability to develop and optimize treatment plans for optimal release of pharmaceuticals in the treatment of cancer. Future work will test and validate these novel delivery and release mechanisms in vivo.

The American Society of Shoulder and Elbow Therapists' consensus statement on rehabilitation following arthroscopic rotator cuff repair.

This is a consensus statement on rehabilitation developed by the American Society of Shoulder and Elbow Therapists. The purpose of this statement is to aid clinical decision making during the rehabilitation of patients after arthroscopic rotator cuff repair. The overarching philosophy of rehabilitation is centered on the principle of the gradual application of controlled stresses to the healing rotator cuff repair with consideration of rotator cuff tear size, tissue quality, and patient variables. This statement describes a rehabilitation framework that includes a 2-week period of strict immobilization and a staged introduction of protected, passive range of motion during weeks 2-6 postoperatively, followed by restoration of active range of motion, and then progressive strengthening beginning at postoperative week 12. When appropriate, rehabilitation continues with a functional progression for return to athletic or demanding work activities. This document represents the first consensus rehabilitation statement developed by a multidisciplinary society of international rehabilitation professionals specifically for the postoperative care of patients after arthroscopic rotator cuff repair.

Efficient potassium diode pumped alkali laser operating in pulsed mode.

This paper presents the results of our experiments on the development of an efficient hydrocarbon free diode pumped alkali laser based on potassium vapor buffered by He gas at 600 Torr. A slope efficiency of more than 50% was demonstrated with a total optical conversion efficiency of 30%. This result was achieved by using a narrowband diode laser stack as the pump source. The stack was operated in pulsed mode to avoid limiting thermal effects and ionization.

Time-resolved multi-omics reveals diverse metabolic strategies of Salmonella during diet-induced inflammation.

With a rise in antibiotic resistance and chronic infection, the metabolic response of Salmonella enterica serovar Typhimurium to various dietary conditions over time remains an understudied avenue for novel, targeted therapeutics. Elucidating how enteric pathogens respond to dietary variation not only helps us decipher the metabolic strategies leveraged for expansion but also assists in proposing targets for therapeutic interventions. In this study, we use a multi-omics approach to identify the metabolic response of Salmonella enterica serovar Typhimurium in mice on both a fibrous diet and high-fat diet over time. When comparing Salmonella gene expression between diets, we found a preferential use of respiratory electron acceptors consistent with increased inflammation in high-fat diet mice. Looking at the high-fat diet over the course of infection, we noticed heterogeneity in samples based on Salmonella ribosomal activity, which is separated into three infection phases: early, peak, and late. We identified key respiratory, carbon, and pathogenesis gene expressions descriptive of each phase. Surprisingly, we identified genes associated with host cell entry expressed throughout infection, suggesting subpopulations of Salmonella or stress-induced dysregulation. Collectively, these results highlight not only the sensitivity of Salmonella to its environment but also identify phase-specific genes that may be used as therapeutic targets to reduce infection.IMPORTANCEIdentifying novel therapeutic strategies for Salmonella infection that occur in relevant diets and over time is needed with the rise of antibiotic resistance and global shifts toward Western diets that are high in fat and low in fiber. Mice on a high-fat diet are more inflamed compared to those on a fibrous diet, creating an environment that results in more favorable energy generation for Salmonella. We observed differential gene expression across infection phases in mice over time on a high-fat diet. Together, these findings reveal the metabolic tuning of Salmonella to dietary and temporal perturbations. Research like this, which explores the dimensions of pathogen metabolic plasticity, can pave the way for rationally designed strategies to control disease.

Time resolved multi-omics reveals diverse metabolic strategies of Salmonella during diet-induced inflammation.

With a rise in antibiotic resistance and chronic infection, the metabolic response of Salmonella enterica serovar Typhimurium to various dietary conditions over time remains an understudied avenue for novel, targeted therapeutics. Elucidating how enteric pathogens respond to dietary variation not only helps us decipher the metabolic strategies leveraged for expansion but also assists in proposing targets for therapeutic interventions. Here, we use a multi-omics approach to identify the metabolic response of Salmonella enterica serovar Typhimurium in mice on both a fibrous diet and high-fat diet over time. When comparing Salmonella gene expression between diets, we found a preferential use of respiratory electron acceptors consistent with increased inflammation of the high-fat diet mice. Looking at the high-fat diet over the course of infection, we noticed heterogeneity of samples based on Salmonella ribosomal activity, which separated into three infection phases: early, peak, and late. We identified key respiratory, carbon, and pathogenesis gene expression descriptive of each phase. Surprisingly, we identified genes associated with host-cell entry expressed throughout infection, suggesting sub-populations of Salmonella or stress-induced dysregulation. Collectively, these results highlight not only the sensitivity of Salmonella to its environment but also identify phase-specific genes that may be used as therapeutic targets to reduce infection. Importance: Identifying novel therapeutic strategies for Salmonella infection that occur in relevant diets and over time is needed with the rise of antibiotic resistance and global shifts towards Western diets that are high in fat and low in fiber. Mice on a high-fat diet are more inflamed compared to those on a fibrous diet, creating an environment that results in more favorable energy generation for Salmonella . Over time on a high-fat diet, we observed differential gene expression across infection phases. Together, these findings reveal the metabolic tuning of Salmonella to dietary and temporal perturbations. Research like this, exploring the dimensions of pathogen metabolic plasticity, can pave the way for rationally designed strategies to control disease.

Gut microbiota carbon and sulfur metabolisms support Salmonella infections.

Salmonella enterica serovar Typhimurium is a pervasive enteric pathogen and ongoing global threat to public health. Ecological studies in the Salmonella impacted gut remain underrepresented in the literature, discounting microbiome mediated interactions that may inform Salmonella physiology during colonization and infection. To understand the microbial ecology of Salmonella remodeling of the gut microbiome, we performed multi-omics on fecal microbial communities from untreated and Salmonella-infected mice. Reconstructed genomes recruited metatranscriptomic and metabolomic data providing a strain-resolved view of the expressed metabolisms of the microbiome during Salmonella infection. These data informed possible Salmonella interactions with members of the gut microbiome that were previously uncharacterized. Salmonella-induced inflammation significantly reduced the diversity of genomes that recruited transcripts in the gut microbiome, yet increased transcript mapping was observed for seven members, among which Luxibacter and Ligilactobacillus transcript read recruitment was most prevalent. Metatranscriptomic insights from Salmonella and other persistent taxa in the inflamed microbiome further expounded the necessity for oxidative tolerance mechanisms to endure the host inflammatory responses to infection. In the inflamed gut lactate was a key metabolite, with microbiota production and consumption reported amongst members with detected transcript recruitment. We also showed that organic sulfur sources could be converted by gut microbiota to yield inorganic sulfur pools that become oxidized in the inflamed gut, resulting in thiosulfate and tetrathionate that support Salmonella respiration. This research advances physiological microbiome insights beyond prior amplicon-based approaches, with the transcriptionally active organismal and metabolic pathways outlined here offering intriguing intervention targets in the Salmonella-infected intestine.

Gut microbiome carbon and sulfur metabolisms support Salmonella during pathogen infection.

Salmonella enterica serovar Typhimurium is a pervasive enteric pathogen and an ongoing global threat to public health. Ecological studies in the Salmonella impacted gut remain underrepresented in the literature, discounting the microbiome mediated interactions that may inform Salmonella physiology during colonization and infection. To understand the microbial ecology of Salmonella remodeling of the gut microbiome, here we performed multi-omics approaches on fecal microbial communities from untreated and Salmonella -infected mice. Reconstructed genomes recruited metatranscriptomic and metabolomic data providing a strain-resolved view of the expressed metabolisms of the microbiome during Salmonella infection. This data informed possible Salmonella interactions with members of the gut microbiome that were previously uncharacterized. Salmonella- induced inflammation significantly reduced the diversity of transcriptionally active members in the gut microbiome, yet increased gene expression was detected for 7 members, with Luxibacter and Ligilactobacillus being the most active. Metatranscriptomic insights from Salmonella and other persistent taxa in the inflamed microbiome further expounded the necessity for oxidative tolerance mechanisms to endure the host inflammatory responses to infection. In the inflamed gut lactate was a key metabolite, with microbiota production and consumption reported amongst transcriptionally active members. We also showed that organic sulfur sources could be converted by gut microbiota to yield inorganic sulfur pools that become oxidized in the inflamed gut, resulting in thiosulfate and tetrathionate that supports Salmonella respiration. Advancement of pathobiome understanding beyond inferences from prior amplicon-based approaches can hold promise for infection mitigation, with the active community outlined here offering intriguing organismal and metabolic therapeutic targets.

A conserved interdomain microbial network underpins cadaver decomposition despite environmental variables.

Microbial breakdown of organic matter is one of the most important processes on Earth, yet the controls of decomposition are poorly understood. Here we track 36 terrestrial human cadavers in three locations and show that a phylogenetically distinct, interdomain microbial network assembles during decomposition despite selection effects of location, climate and season. We generated a metagenome-assembled genome library from cadaver-associated soils and integrated it with metabolomics data to identify links between taxonomy and function. This universal network of microbial decomposers is characterized by cross-feeding to metabolize labile decomposition products. The key bacterial and fungal decomposers are rare across non-decomposition environments and appear unique to the breakdown of terrestrial decaying flesh, including humans, swine, mice and cattle, with insects as likely important vectors for dispersal. The observed lockstep of microbial interactions further underlies a robust microbial forensic tool with the potential to aid predictions of the time since death.

Where does Neisseria acquire foreign DNA from: an examination of the source of genomic and pathogenic islands and the evolution of the Neisseria genus.

BACKGROUND: Pathogenicity islands (PAIs) or genomic islands (GEIs) are considered to be the result of a recent horizontal transfer. Detecting PAIs/GEIs as well as their putative source can provide insight into the organism's pathogenicity within its host. Previously we introduced a tool called S-plot which provides a visual representation of the variation in compositional properties across and between genomic sequences. Utilizing S-plot and new functionality developed here, we examined 18 publicly available Neisseria genomes, including strains of both pathogenic and non-pathogenic species, in order to identify regions of unusual compositional properties (RUCPs) using both a sliding window as well as a gene-by-gene approach. RESULTS: Numerous GEIs and PAIs were identified including virulence genes previously found within the pathogenic Neisseria species. While some genes were conserved amongst all species, only pathogenic species, or an individual species, a number of genes were detected that are unique to an individual strain. While the majority of such genes have an origin unknown, a number of putative sources including pathogenic and capsule-containing bacteria were determined, indicative of gene exchange between Neisseria spp. and other bacteria within their microhabitat. Furthermore, we uncovered evidence that both N. meningitidis and N. gonorrhoeae have separately acquired DNA from their human host. Data suggests that all three Neisseria species have received horizontally transferred elements post-speciation. CONCLUSIONS: Using this approach, we were able to not only find previously identified regions of virulence but also new regions which may be contributing to the virulence of the species. This comparative analysis provides a means for tracing the evolutionary history of the acquisition of foreign DNA within this genus. Looking specifically at the RUCPs present within the 18 genomes considered, a stronger similarity between N. meningitidis and N. lactamica is observed, suggesting that N. meningitidis arose before N. gonorrhoeae.

SCNIC: Sparse correlation network investigation for compositional data.

Microbiome studies are often limited by a lack of statistical power due to small sample sizes and a large number of features. This problem is exacerbated in correlative studies of multi-omic datasets. Statistical power can be increased by finding and summarizing modules of correlated observations, which is one dimensionality reduction method. Additionally, modules provide biological insight as correlated groups of microbes can have relationships among themselves. To address these challenges, we developed SCNIC: Sparse Cooccurrence Network Investigation for compositional data. SCNIC is open-source software that can generate correlation networks and detect and summarize modules of highly correlated features. Modules can be formed using either the Louvain Modularity Maximization (LMM) algorithm or a Shared Minimum Distance algorithm (SMD) that we newly describe here and relate to LMM using simulated data. We applied SCNIC to two published datasets and we achieved increased statistical power and identified microbes that not only differed across groups, but also correlated strongly with each other, suggesting shared environmental drivers or cooperative relationships among them. SCNIC provides an easy way to generate correlation networks, identify modules of correlated features and summarize them for downstream statistical analysis. Although SCNIC was designed considering properties of microbiome data, such as compositionality and sparsity, it can be applied to a variety of data types including metabolomics data and used to integrate multiple data types. SCNIC allows for the identification of functional microbial relationships at scale while increasing statistical power through feature reduction.