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Funder: National Science Foundation; Id: http://dx.doi.org/10.13039/501100008982, 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.

BackgroundHelicobacter pylori, a bacterium that infects approximately half of the world’s population, is associated with various gastrointestinal diseases, including peptic ulcers, non-ulcer dyspepsia, gastric adenocarcinoma, and gastric lymphoma. To combat the increasing antibiotic resistance of H. pylori, the need for new therapeutic strategies has become more pressing. Characterization of the interactions between H. pylori and the fecal microbiome, as well as the mechanisms underlying these interactions, may offer new therapeutic approaches. Exploration of changes in fatty acid metabolism associated with H. pylori-mediated alterations of the fecal microbiome may also reveal strategies to help prevent progression to neoplasia.AimTo characterize the gut microbiome and metabolome in H. pylori patients in a socioeconomically challenged and underprivileged inner-city community.MethodsStool samples from 19 H. pylori patients and 16 control subjects were analyzed. 16S rRNA gene sequencing was performed on normalized pooled amplicons using the Illumina MiSeq System using a MiSeq reagent kit v2. Alpha and beta diversity analyses were performed in QIIME 2. Non-targeted fatty acid analysis of the samples was carried out using gas chromatography-mass spectrometry (GC-MS), which measures the total content of 30 fatty acids in stool after conversion into their corresponding fatty acid methyl esters. Multi-dimensional scaling (MDS) was performed on Bray-Curtis distance matrices created from both the metabolomics and microbiome datasets and a Procrustes test was performed on the metabolomics and microbiome MDS coordinates.ResultsFecal microbiome analysis showed that alpha diversity was lowest in H. pylori patients over 40 years of age compared to control subjects of similar age group. Beta diversity analysis of the samples revealed significant differences in microbial community structure between H. pylori patients and control subjects. Thirty-eight and six taxa had lower and higher relative abundance in H. pylori patients, respectively. Taxa that were enriched in H. pylori patients included Atopobium, Gemellaceae, Micrococcaceae, Gemellales and Rothia (R. mucilaginosa). Notably, relative abundance of the phylum Verrucomicrobia was decreased in H. pylori patients compared to control subjects, suggesting disruption of the gut mucosal environment by H. pylori. Procrustes analysis showed a significant relationship between the microbiome and metabolome datasets. Stool samples from H. pylori patients showed increases in several fatty acids including the polyunsaturated fatty acids (PUFAs) 22:4n6, 22:5n3, 20:3n6 and 22:2n6, while decreases were noted in other fatty acids including the PUFA 18:3n6. The pattern of changes in fatty acid concentration correlated to the Bacteroidetes:Firmicutes ratio determined by 16S rRNA gene analysis.ConclusionAn individualized understanding of gut microbiome features among H. pylori patients will pave the way for improved community impact, reduced healthcare burdens of repeated treatment, and decreased mounting resistance.

A digital computer program written by Westinghouse engineers aids the design of new static logic systems for metal mills. The computer program accepts logic equations specifying the functions to be performed. The program generates circuit drawings of logic circuitry plus paper tape for controlling an automatic wiring machine. The automatic wiring machine wires the back panel receptacles for plug-in cards which contain the integrated-circuit AND/NAND logic elements. The design engineer determines functional requirements to be performed by the logic circuits. He expresses these requirements in convenient logic statement form and adds comments on circuit functioning. Logic and comment statements are keypunched for input to the computer. The computer analyzes the equations, converts the functions to AND/ NAND logic circuits and generates the punched-paper tape required by the automatic wiring machine. In addition, the computer generates the circuit diagrams showing the actual AND/NAND circuits with complete (JIC) cross references and comments on circuit functioning. During computer processing diagnostic messages are generated to aid in finding design errors. Only one type of logic board is used in the new static logic system to perform all logic functions. This reduces maintenance to mere board replacement and greatly reduces spare board inventory. Triac outputs are featured in addition to normal contact closure outputs. Traic is a static ac switch which can operate pilot-operated solenoid valves, contactors, indicating lights, and other common ac loads. The logic system combines the advantages of fully wired integrated-circuit logic with the convenience and accuracy of computer aided design.