scholarly journals Detection of Multidimensional Co-Exclusion Patterns in Microbial Communities

2018 ◽  
Author(s):  
Levent Albayrak ◽  
Kamil Khanipov ◽  
George Golovko ◽  
Yuriy Fofanov

AbstractMotivationIdentification of complex relationships among members of microbial communities is key to understand and control the microbiota. Co-exclusion is arguably one of the most important patterns reflecting microorganisms’ intolerance to each other’s presence. Knowing these relations opens an opportunity to manipulate microbiotas, personalize anti-microbial and probiotic treatments as well as guide microbiota transplantation. The co-exclusion pattern however, cannot be appropriately described by a linear function nor its strength be estimated using covariance or (negative) Pearson and Spearman correlation coefficients. This manuscript proposes a way to quantify the strength and evaluate the statistical significance of co-exclusion patterns between two, three or more variables describing a microbiota and allows one to extend analysis beyond microorganism abundance by including other microbiome associated measurements such as, pH, temperature etc., as well as estimate the expected numbers of false positive co-exclusion patterns in a co-exclusion network.ResultsThe implemented computational pipeline (CoEx) tested against 2,380 microbial profiles (samples) from The Human Microbiome Project resulted in body-site specific pairwise co-exclusion patterns.AvailabilityC++ source code for calculation of the score and p-value for 2, 3, and 4 dimensional co-exclusion patterns as well as source code and executable files for the CoEx pipeline are available at https://scsb.utmb.edu/labgroups/fofanov/co-exclusion_in_microbial_communities.aspContactlealbayr@utmb.eduSupplementary informationSupplementary data are available at biorxiv online.

2019 ◽  
Author(s):  
Golovko George ◽  
Khanipov Kamil ◽  
Albayrak Levent ◽  
Fofanov Yuriy

AbstractMotivationIdentification of complex relationships within members of microbial communities is key to understand and guide microbial transplantation and provide personalized anti-microbial and probiotic treatments. Since members of a given microbial community can be simultaneously involved in multiple relations that altogether will determine their abundance, not all significant relations between organisms are expected to be manifested as visually uninterrupted patterns and be detected using traditional correlation nor mutual information coefficient based approaches.ResultsThis manuscript proposes a pattern specific way to quantify the strength and estimate the statistical significance of two-dimensional co-presence, co-exclusion, and one-way relations patterns between abundance profiles of two organisms which can be extended to three or more dimensional patterns. Presented approach can also be extended by including a variety of physical (pH, temperature, oxygen concentration) and biochemical (antimicrobial susceptibility, nutrient and metabolite concentration) variables into the search for multidimensional patterns. The presented approach has been tested using 2,380 microbiome samples from the Human Microbiome Project resulting in body-site specific networks of statistically significant 2D patterns. We also were able to demonstrate the presence of several 3D patterns in the Human Microbiome Project data.AvailabilityC++ source code for two and three-dimensional patterns, as well as executable files for the Pickle pipeline, are in the attached supplementary [email protected]


2021 ◽  
Vol 43 (3) ◽  
pp. 2135-2146
Author(s):  
Mahmoud A. Ghannoum ◽  
Thomas S. McCormick ◽  
Mauricio Retuerto ◽  
Gurkan Bebek ◽  
Susan Cousineau ◽  
...  

Gastrointestinal microbiome dysbiosis may result in harmful effects on the host, including those caused by inflammatory bowel diseases (IBD). The novel probiotic BIOHM, consisting of Bifidobacterium breve, Saccharomyces boulardii, Lactobacillus acidophilus, L. rhamnosus, and amylase, was developed to rebalance the bacterial–fungal gut microbiome, with the goal of reducing inflammation and maintaining a healthy gut population. To test the effect of BIOHM on human subjects, we enrolled a cohort of 49 volunteers in collaboration with the Fermentation Festival group (Santa Barbara, CA, USA). The profiles of gut bacterial and fungal communities were assessed via stool samples collected at baseline and following 4 weeks of once-a-day BIOHM consumption. Mycobiome analysis following probiotic consumption revealed an increase in Ascomycota levels in enrolled individuals and a reduction in Zygomycota levels (p value < 0.01). No statistically significant difference in Basidiomycota was detected between pre- and post-BIOHM samples and control abundance profiles (p > 0.05). BIOHM consumption led to a significant reduction in the abundance of Candida genus in tested subjects (p value < 0.013), while the abundance of C. albicans also trended lower than before BIOHM use, albeit not reaching statistical significance. A reduction in the abundance of Firmicutes at the phylum level was observed following BIOHM use, which approached levels reported for control individuals reported in the Human Microbiome Project data. The preliminary results from this clinical study suggest that BIOHM is capable of significantly rebalancing the bacteriome and mycobiome in the gut of healthy individuals, suggesting that further trials examining the utility of the BIOHM probiotic in individuals with gastrointestinal symptoms, where dysbiosis is considered a source driving pathogenesis, are warranted.


Microbiome ◽  
2020 ◽  
Vol 8 (1) ◽  
Author(s):  
George Golovko ◽  
Khanipov Kamil ◽  
Levent Albayrak ◽  
Anna M. Nia ◽  
Renato Salomon Arroyo Duarte ◽  
...  

Abstract Background Identification of complex multidimensional interaction patterns within microbial communities is the key to understand, modulate, and design beneficial microbiomes. Every community has members that fulfill an essential function affecting multiple other community members through secondary metabolism. Since microbial community members are often simultaneously involved in multiple relations, not all interaction patterns for such microorganisms are expected to exhibit a visually uninterrupted pattern. As a result, such relations cannot be detected using traditional correlation, mutual information, principal coordinate analysis, or covariation-based network inference approaches. Results We present a novel pattern-specific method to quantify the strength and estimate the statistical significance of two-dimensional co-presence, co-exclusion, and one-way relation patterns between abundance profiles of two organisms as well as extend this approach to allow search and visualize three-, four-, and higher dimensional patterns. The proposed approach has been tested using 2380 microbiome samples from the Human Microbiome Project resulting in body site-specific networks of statistically significant 2D patterns as well as revealed the presence of 3D patterns in the Human Microbiome Project data. Conclusions The presented study suggested that search for Boolean patterns in the microbial abundance data needs to be pattern specific. The reported presence of multidimensional patterns (which cannot be reduced to a combination of two-dimensional patterns) suggests that multidimensional (multi-organism) relations may play important roles in the organization of microbial communities, and their detection (and appropriate visualization) may lead to a deeper understanding of the organization and dynamics of microbial communities.


2019 ◽  
Vol 36 (4) ◽  
pp. 1289-1290
Author(s):  
Patrick H Bradley ◽  
Katherine S Pollard

Abstract Summary Phylogenetic comparative methods are powerful but presently under-utilized ways to identify microbial genes underlying differences in community composition. These methods help to identify functionally important genes because they test for associations beyond those expected when related microbes occupy similar environments. We present phylogenize, a pipeline with web, QIIME 2 and R interfaces that allows researchers to perform phylogenetic regression on 16S amplicon and shotgun sequencing data and to visualize results. phylogenize applies broadly to both host-associated and environmental microbiomes. Using Human Microbiome Project and Earth Microbiome Project data, we show that phylogenize draws similar conclusions from 16S versus shotgun sequencing and reveals both known and candidate pathways associated with host colonization. Availability and implementation phylogenize is available at https://phylogenize.org and https://bitbucket.org/pbradz/phylogenize. Supplementary information Supplementary data are available at Bioinformatics online.


2015 ◽  
Vol 32 (6) ◽  
pp. 867-874 ◽  
Author(s):  
Matthew B. Biggs ◽  
Jason A. Papin

Abstract Motivation: Most microbes on Earth have never been grown in a laboratory, and can only be studied through DNA sequences. Environmental DNA sequence samples are complex mixtures of fragments from many different species, often unknown. There is a pressing need for methods that can reliably reconstruct genomes from complex metagenomic samples in order to address questions in ecology, bioremediation, and human health. Results: We present the SOrting by NEtwork Completion (SONEC) approach for assigning reactions to incomplete metabolic networks based on a metabolite connectivity score. We successfully demonstrate proof of concept in a set of 100 genome-scale metabolic network reconstructions, and delineate the variables that impact reaction assignment accuracy. We further demonstrate the integration of SONEC with existing approaches (such as cross-sample scaffold abundance profile clustering) on a set of 94 metagenomic samples from the Human Microbiome Project. We show that not only does SONEC aid in reconstructing species-level genomes, but it also improves functional predictions made with the resulting metabolic networks. Availability and implementation: The datasets and code presented in this work are available at: https://bitbucket.org/mattbiggs/sorting_by_network_completion/. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.


2019 ◽  
Vol 18 (3) ◽  
pp. 270-313
Author(s):  
Nathália Haib Costa Pereira ◽  
Ana Lúcia De Mattia

Introducción: La hipotermia es un evento común en el período intraoperatorio, acarrea consecuencias en la recuperación del paciente, con complicaciones en diversos sistemas del organismo, como el cardíaco, respiratorio, tegumentario, digestivo, inmunológico y también el sistema de coagulación.Objetivo: Analizar las complicaciones presentadas por el paciente en el período postoperatorio relacionadas con la hipotermia intraoperatoria.Métodos: Estudio de cohorte retrospectivo, muestra compuesta por 54 registros clínicos de pacientes, participantes de un estudio anterior, de diseño experimental, en que fueron sometidos o no a infusión de solución intravenosa caliente, en el período intraoperatorio y de recuperación anestésica. Las variables fueron analizadas en 4 tiempos diferentes, a la llegada a la Unidad de Internación, después de 17, 32 y 108 horas de período postoperatorio. El modelo utilizado fue el logístico marginal.Resultados: La mayoría de los pacientes 40 (74,07%) eran del sexo femenino, edad media de 47,06 años, y 42 (77,78%) salieron normotérmicos de la Sala de Recuperación Post-Anestésica, con temperatura media de 36,2ºC. En cuanto a la comparación de las variables entre los grupos de pacientes normotérmicos e hipotérmicos, a lo largo del tiempo, las variables que presentaron significancia estadística fueron el tiempo de internación, dolor, náusea y herida operatoria con presencia de secreción, con p-valor menor que 0.05.Conclusión: Ante las complicaciones encontradas en este estudio, se hace necesario el desarrollo de acciones de prevención y control de la hipotermia intraoperatoria buscando una mejor recuperación del paciente en el período de postoperatorio. Introduction: Hypothermia is a common event in the intraoperative period, it triggers consequences in the recovery of the patient, with complications in several systems of the organism, such as cardiac, respiratory, integumentary, digestive, immunological and also the coagulation system.Objective: To analyze the complications presented by the patient in the postoperative period related to intraoperative hypothermia.Methods: A retrospective cohort study was carried out in a sample composed of 54 patients' files, from a previous experimental study, in which they were submitted or not to warmed intraoperative intravenous infusion and anesthetic recovery. The variables were analyzed at 4 different times, upon arrival at the hospitalization unit, after 17, 32 and 108 hours postoperative. The model used was the marginal logistics.Results: The majority of patients 40 (74.07%) were female, mean age of 47.06 years, 42 (77.78%) were normothermic patients from the Post Anesthesia Recovery Room, with a mean temperature of 36.2ºC. Regarding the comparison between variables and groups of normothermic and hypothermic patients, over time, the variables that presented statistical significance were the time of hospitalization, pain, nausea, evacuation and surgical wound with presence of secretion, with a p-value less than 0.05.Conclusion: In view of the complications found in this study, it is necessary to develop preventive and control actions for intraoperative hypothermia aiming at a better recovery of the patient in the postoperative period. Introdução: A hipotermia é um evento comum no período intraoperatório, acarreta consequências na recuperação do paciente, com complicações em diversos sistemas do organismo, como o cardíaco, respiratório, tegumentar, digestório, imunológico e também o sistema de coagulação. Objetivo: Analisar as complicações apresentadas pelo paciente no período de pós-operatório relacionadas com a hipotermia intraoperatória.Métodos: Estudo de coorte retrospectivo, amostra composta por 54 prontuários de pacientes, participantes de um estudo anterior, de delineamento experimental, em que foram submetidos ou não à infusão venosa aquecida no período intraoperatório e de recuperação anestésica. As variáveis foram analisadas em 4 tempos diferentes, na chegada a Unidade de Internação, após 17, 32 e 108 horas de período pós-operatório. O modelo utilizado foi o logístico marginal.Resultados: A maioria dos pacientes 40 (74,07%) eram do sexo feminino, com média de idade de 47,06 anos, e 42 (77,78%) saíram normotérmicos da Sala de Recuperação Pós-Anestésica, com temperatura média de 36,2ºC. Em relação à comparação entre as variáveis e os grupos de pacientes normotérmicos e hipotérmicos, ao longo do tempo, as variáveis que apresentaram significância estatística foram o tempo de internação, dor, náusea, evacuação e aspecto da ferida operatória com presença de secreção, com p-valor menor que 0,05. Conclusão: Diante das complicações encontradas neste estudo, faz-se necessário o desenvolvimento de ações de prevenção e controle da hipotermia intraoperatória visando uma melhor recuperação do paciente no período de pós-operatório.


2020 ◽  
Vol 16 ◽  
Author(s):  
Amal A. Mohamed ◽  
Mokhtar M. El-Zawahry ◽  
Omnia I. Tantawi ◽  
Amyan Aalkhalegy ◽  
Lamiaa Abdelfattah Fathalla ◽  
...  

Background:: In the early stages of HCC, it is unsatisfactory to depend on alpha-fetoprotein for diagnosis. Objective:: The current study evaluated the possibility of the two miRNAs which are miRNA-96 and miRNA-224 to act as biomarkers for HCC diagnosis. Methods:: This study included 50 patients with HCV-induced HCC and 50 patients with HCV-induced liver cirrhosis for comparison as well as 67 healthy volunteers as controls. All participants were subjected to history taking, clinical examination, and laboratory investigations as well as quantification of serum miRNA-96 and miRNA-224 by real-time quantitative PCR. Results:: MicroRNA 224 level was significantly higher in HCC than the other two groups and was significantly higher in liver cirrhosis than the control group. MicroRNA 96 level was higher in HCC than the control group and was higher in cirrhotic group than both HCC and control groups. However, it doesn’t reach the statistical significance level. The best cut-off value of microRNA 96 for detecting HCC was 3.414 with a sensitivity of 67% and a specificity of 67%, (p-value <0.001). The best cut-off value of microRNA 224 for detecting HCC was 16.75 with a sensitivity of 88% and a specificity of 85% (p-value<0.001). Conclusion:: miRNA-224 could serve as a biomarker for the HCC diagnosis.


2019 ◽  
Vol 35 (19) ◽  
pp. 3592-3598 ◽  
Author(s):  
Justin G Chitpin ◽  
Aseel Awdeh ◽  
Theodore J Perkins

Abstract Motivation Chromatin Immunopreciptation (ChIP)-seq is used extensively to identify sites of transcription factor binding or regions of epigenetic modifications to the genome. A key step in ChIP-seq analysis is peak calling, where genomic regions enriched for ChIP versus control reads are identified. Many programs have been designed to solve this task, but nearly all fall into the statistical trap of using the data twice—once to determine candidate enriched regions, and again to assess enrichment by classical statistical hypothesis testing. This double use of the data invalidates the statistical significance assigned to enriched regions, thus the true significance or reliability of peak calls remains unknown. Results Using simulated and real ChIP-seq data, we show that three well-known peak callers, MACS, SICER and diffReps, output biased P-values and false discovery rate estimates that can be many orders of magnitude too optimistic. We propose a wrapper algorithm, RECAP, that uses resampling of ChIP-seq and control data to estimate a monotone transform correcting for biases built into peak calling algorithms. When applied to null hypothesis data, where there is no enrichment between ChIP-seq and control, P-values recalibrated by RECAP are approximately uniformly distributed. On data where there is genuine enrichment, RECAP P-values give a better estimate of the true statistical significance of candidate peaks and better false discovery rate estimates, which correlate better with empirical reproducibility. RECAP is a powerful new tool for assessing the true statistical significance of ChIP-seq peak calls. Availability and implementation The RECAP software is available through www.perkinslab.ca or on github at https://github.com/theodorejperkins/RECAP. Supplementary information Supplementary data are available at Bioinformatics online.


F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 103 ◽  
Author(s):  
Subina Mehta ◽  
Marie Crane ◽  
Emma Leith ◽  
Bérénice Batut ◽  
Saskia Hiltemann ◽  
...  

The Human Microbiome Project (HMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the ‘microbiome’) in human health and disease. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). Conversely, metatranscriptomics, the study of a microbial community’s RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome.  In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking.  In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.


2010 ◽  
Vol 26 (7) ◽  
pp. 889-895 ◽  
Author(s):  
Jinrui Xu ◽  
Yang Zhang

Abstract Motivation: Protein structure similarity is often measured by root mean squared deviation, global distance test score and template modeling score (TM-score). However, the scores themselves cannot provide information on how significant the structural similarity is. Also, it lacks a quantitative relation between the scores and conventional fold classifications. This article aims to answer two questions: (i) what is the statistical significance of TM-score? (ii) What is the probability of two proteins having the same fold given a specific TM-score? Results: We first made an all-to-all gapless structural match on 6684 non-homologous single-domain proteins in the PDB and found that the TM-scores follow an extreme value distribution. The data allow us to assign each TM-score a P-value that measures the chance of two randomly selected proteins obtaining an equal or higher TM-score. With a TM-score at 0.5, for instance, its P-value is 5.5 × 10−7, which means we need to consider at least 1.8 million random protein pairs to acquire a TM-score of no less than 0.5. Second, we examine the posterior probability of the same fold proteins from three datasets SCOP, CATH and the consensus of SCOP and CATH. It is found that the posterior probability from different datasets has a similar rapid phase transition around TM-score=0.5. This finding indicates that TM-score can be used as an approximate but quantitative criterion for protein topology classification, i.e. protein pairs with a TM-score &gt;0.5 are mostly in the same fold while those with a TM-score &lt;0.5 are mainly not in the same fold. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.


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