scholarly journals Adaptive and powerful microbiome multivariate association analysis via feature selection

2022 ◽  
Vol 4 (1) ◽  
Author(s):  
Kalins Banerjee ◽  
Jun Chen ◽  
Xiang Zhan

ABSTRACT The important role of human microbiome is being increasingly recognized in health and disease conditions. Since microbiome data is typically high dimensional, one popular mode of statistical association analysis for microbiome data is to pool individual microbial features into a group, and then conduct group-based multivariate association analysis. A corresponding challenge within this approach is to achieve adequate power to detect an association signal between a group of microbial features and the outcome of interest across a wide range of scenarios. Recognizing some existing methods’ susceptibility to the adverse effects of noise accumulation, we introduce the Adaptive Microbiome Association Test (AMAT), a novel and powerful tool for multivariate microbiome association analysis, which unifies both blessings of feature selection in high-dimensional inference and robustness of adaptive statistical association testing. AMAT first alleviates the burden of noise accumulation via distance correlation learning, and then conducts a data-adaptive association test under the flexible generalized linear model framework. Extensive simulation studies and real data applications demonstrate that AMAT is highly robust and often more powerful than several existing methods, while preserving the correct type I error rate. A free implementation of AMAT in R computing environment is available at https://github.com/kzb193/AMAT.

2019 ◽  
Author(s):  
Chan Wang ◽  
Jiyuan Hu ◽  
Martin J Blaser ◽  
Huilin Li

Abstract Motivation Recent microbiome association studies have revealed important associations between microbiome and disease/health status. Such findings encourage scientists to dive deeper to uncover the causal role of microbiome in the underlying biological mechanism, and have led to applying statistical models to quantify causal microbiome effects and to identify the specific microbial agents. However, there are no existing causal mediation methods specifically designed to handle high dimensional and compositional microbiome data. Results We propose a rigorous Sparse Microbial Causal Mediation Model (SparseMCMM) specifically designed for the high dimensional and compositional microbiome data in a typical three-factor (treatment, microbiome and outcome) causal study design. In particular, linear log-contrast regression model and Dirichlet regression model are proposed to estimate the causal direct effect of treatment and the causal mediation effects of microbiome at both the community and individual taxon levels. Regularization techniques are used to perform the variable selection in the proposed model framework to identify signature causal microbes. Two hypothesis tests on the overall mediation effect are proposed and their statistical significance is estimated by permutation procedures. Extensive simulated scenarios show that SparseMCMM has excellent performance in estimation and hypothesis testing. Finally, we showcase the utility of the proposed SparseMCMM method in a study which the murine microbiome has been manipulated by providing a clear and sensible causal path among antibiotic treatment, microbiome composition and mouse weight. Availability and implementation https://sites.google.com/site/huilinli09/software and https://github.com/chanw0/SparseMCMM. Supplementary information Supplementary data are available at Bioinformatics online.


2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Huang Lin ◽  
Shyamal Das Peddada

AbstractIncreasingly, researchers are discovering associations between microbiome and a wide range of human diseases such as obesity, inflammatory bowel diseases, HIV, and so on. The first step towards microbiome wide association studies is the characterization of the composition of human microbiome under different conditions. Determination of differentially abundant microbes between two or more environments, known as differential abundance (DA) analysis, is a challenging and an important problem that has received considerable interest during the past decade. It is well documented in the literature that the observed microbiome data (OTU/SV table) are relative abundances with an excess of zeros. Since relative abundances sum to a constant, these data are necessarily compositional. In this article we review some recent methods for DA analysis and describe their strengths and weaknesses.


2019 ◽  
Author(s):  
Chan Wang ◽  
Jiyuan Hu ◽  
Martin J. Blaser ◽  
Huilin Li

AbstractMotivationRecent microbiome association studies have revealed important associations between microbiome and disease/health status. Such findings encourage scientists to dive deeper to uncover the causal role of microbiome in the underlying biological mechanism, and have led to applying statistical models to quantify causal microbiome effects and to identify the specific microbial agents. However, there are no existing causal mediation methods specifically designed to handle high dimensional and compositional microbiome data.ResultsWe propose a rigorous Sparse Microbial Causal Mediation Model (SparseMCMM) specifically designed for the high dimensional and compositional microbiome data in a typical three-factor (treatment, microbiome and outcome) causal study design. In particular, linear log-contrast regression model and Dirichlet regression model are proposed to estimate the causal direct effect of treatment and the causal mediation effects of microbiome at both the community and individual taxon levels. Regularization techniques are used to perform the variable selection in the proposed model framework to identify signature causal microbes. Two hypothesis tests on the overall mediation effect are proposed and their statistical significance is estimated by permutation procedures. Extensive simulated scenarios show that SparseMCMM has excellent performance in estimation and hypothesis testing. Finally, we showcase the utility of the proposed SparseMCMM method in a study which the murine microbiome has been manipulated by providing a clear and sensible causal path among antibiotic treatment, microbiome composition and mouse weight.


2021 ◽  
Author(s):  
Andrew Hinton ◽  
Peter J. Mucha

Abstract Background: Numerous metagenomic studies aim to discover associations between the microbial composition of an environment (e.g. Gut, Skin, Oral) and a phenotype of interest. Multivariate analysis (MVA) is often performed in these studies without critical a priori knowledge of which taxa are associated with the phenotype being studied. Consequently, non-parametric MVA methods are applied directly to all taxa surveyed independent of noise. This approach typically reduces statistical power in settings where true associations among only a few taxa are obscured by high dimensionality (i.e. sparse association signals). At the same time, the inclusion of all taxa can confound the extraction of key biological insights. Further, low sample size and compositional sample space constraints exist in these data whereby beyond-study generalizability may be reduced if not properly accounted for. More powerful association tests that are interpretable and directly account for compositional constraints while detecting sparse association signals are needed.Methods: We developed Selection-Energy-Permutation (SelEnergyPerm), a non-parametric group association test with embedded feature selection. SelEnergyPerm directly accounts for compositional constraints by selecting parsimonious log ratio signatures from the set of all pairwise log ratios (PLR) between features (OTUs, taxa, etc.). To do this, network methods are used to rank, select, and maximize the between-group association of a candidate log ratio subset. This process is then repeated with an appropriate permutation testing design to simultaneously determine the significance of the selected signatures and association.Results: Simulation results show SelEnergyPerm selects small independent sets of log ratios that capture strong associations in a range of scenarios with small and large dimensional feature spaces. Additionally, our simulation results demonstrate SelEnergyPerm consistently detects/rejects associations in synthetic data with sparse, dense, or no association signals. We demonstrate the novel benefits of our method in four case studies utilizing publicly available 16S rRNA and whole-genome sequencing datasets.Conclusions: Tools to analyze complex high-dimensional metagenomic datasets with sparse association signals using robust PLR have not been sufficiently developed previously. We propose SelEnergyPerm, a novel framework for the discovery of phenotype-associated, metagenomic log ratio signatures for characterizing and understanding alterations in microbial community structure. SelEnergyPerm is implemented in R, available at https://github.com/andrew84830813/selEnergyPermR.


Author(s):  
А.Р. Зарипова ◽  
Л.Р. Нургалиева ◽  
А.В. Тюрин ◽  
И.Р. Минниахметов ◽  
Р.И. Хусаинова

Проведено исследование гена интерферон индуцированного трансмембранного белка 5 (IFITM5) у 99 пациентов с несовершенным остеогенезом (НО) из 86 неродственных семей. НО - клинически и генетически гетерогенное наследственное заболевание соединительной ткани, основное клиническое проявление которого - множественные переломы, начиная с неонатального периода жизни, зачастую приводящие к инвалидизации с детского возраста. К основным клиническим признакам НО относятся голубые склеры, потеря слуха, аномалия дентина, повышенная ломкость костей, нарушения роста и осанки с развитием характерных инвалидизирующих деформаций костей и сопутствующих проблем, включающих дыхательные, неврологические, сердечные, почечные нарушения. НО встречается как у мужчин, так и у женщин. До сих пор не определена степень генетической гетерогенности заболевания. На сегодняшний день известно 20 генов, вовлеченных в патогенез НО, и исследователи разных стран продолжают искать новые гены. В последнее десятилетие стало известно, что аутосомно-рецессивные, аутосомно-доминантные и Х-сцепленные мутации в широком спектре генов, кодирующих белки, которые участвуют в синтезе коллагена I типа, его процессинге, секреции и посттрансляционной модификации, а также в белках, которые регулируют дифференцировку и активность костеобразующих клеток, вызывают НО. Мутации в гене IFITM5, также называемом BRIL (bone-restricted IFITM-like protein), участвующем в формировании остеобластов, приводят к развитию НО типа V. До 5% пациентов имеют НО типа V, который характеризуется образованием гиперпластического каллуса после переломов, кальцификацией межкостной мембраны предплечья и сетчатым рисунком ламелирования, наблюдаемого при гистологическом исследовании кости. В 2012 г. гетерозиготная мутация (c.-14C> T) в 5’-нетранслируемой области (UTR) гена IFITM5 была идентифицирована как основная причина НО V типа. В представленной работе проведен анализ гена IFITM5 и идентифицирована мутация c.-14C>T, возникшая de novo, у одного пациента с НО, которому впоследствии был установлен V тип заболевания. Также выявлены три известных полиморфных варианта: rs57285449; c.80G>C (p.Gly27Ala) и rs2293745; c.187-45C>T и rs755971385 c.279G>A (p.Thr93=) и один ранее не описанный вариант: c.128G>A (p.Ser43Asn) AGC>AAC (S/D), которые не являются патогенными. В статье уделяется внимание особенностям клинических проявлений НО V типа и рекомендуется определение мутации c.-14C>T в гене IFITM5 при подозрении на данную форму заболевания. A study was made of interferon-induced transmembrane protein 5 gene (IFITM5) in 99 patients with osteogenesis imperfecta (OI) from 86 unrelated families and a search for pathogenic gene variants involved in the formation of the disease phenotype. OI is a clinically and genetically heterogeneous hereditary disease of the connective tissue, the main clinical manifestation of which is multiple fractures, starting from the natal period of life, often leading to disability from childhood. The main clinical signs of OI include blue sclera, hearing loss, anomaly of dentin, increased fragility of bones, impaired growth and posture, with the development of characteristic disabling bone deformities and associated problems, including respiratory, neurological, cardiac, and renal disorders. OI occurs in both men and women. The degree of genetic heterogeneity of the disease has not yet been determined. To date, 20 genes are known to be involved in the pathogenesis of OI, and researchers from different countries continue to search for new genes. In the last decade, it has become known that autosomal recessive, autosomal dominant and X-linked mutations in a wide range of genes encoding proteins that are involved in the synthesis of type I collagen, its processing, secretion and post-translational modification, as well as in proteins that regulate the differentiation and activity of bone-forming cells cause OI. Mutations in the IFITM5 gene, also called BRIL (bone-restricted IFITM-like protein), involved in the formation of osteoblasts, lead to the development of OI type V. Up to 5% of patients have OI type V, which is characterized by the formation of a hyperplastic callus after fractures, calcification of the interosseous membrane of the forearm, and a mesh lamellar pattern observed during histological examination of the bone. In 2012, a heterozygous mutation (c.-14C> T) in the 5’-untranslated region (UTR) of the IFITM5 gene was identified as the main cause of OI type V. In the present work, the IFITM5 gene was analyzed and the de novo c.-14C> T mutation was identified in one patient with OI who was subsequently diagnosed with type V of the disease. Three known polymorphic variants were also identified: rs57285449; c.80G> C (p.Gly27Ala) and rs2293745; c.187-45C> T and rs755971385 c.279G> A (p.Thr93 =) and one previously undescribed variant: c.128G> A (p.Ser43Asn) AGC> AAC (S / D), which were not pathogenic. The article focuses on the features of the clinical manifestations of OI type V, and it is recommended to determine the c.-14C> T mutation in the IFITM5 gene if this form of the disease is suspected.


2020 ◽  
Vol 20 (12) ◽  
pp. 1074-1092 ◽  
Author(s):  
Rammohan R.Y. Bheemanaboina

Phosphoinositide 3-kinases (PI3Ks) are a family of ubiquitously distributed lipid kinases that control a wide variety of intracellular signaling pathways. Over the years, PI3K has emerged as an attractive target for the development of novel pharmaceuticals to treat cancer and various other diseases. In the last five years, four of the PI3K inhibitors viz. Idelalisib, Copanlisib, Duvelisib, and Alpelisib were approved by the FDA for the treatment of different types of cancer and several other PI3K inhibitors are currently under active clinical development. So far clinical candidates are non-selective kinase inhibitors with various off-target liabilities due to cross-reactivities. Hence, there is a need for the discovery of isoform-selective inhibitors with improved efficacy and fewer side-effects. The development of isoform-selective inhibitors is essential to reveal the unique functions of each isoform and its corresponding therapeutic potential. Although the clinical effect and relative benefit of pan and isoformselective inhibition will ultimately be determined, with the development of drug resistance and the demand for next-generation inhibitors, it will continue to be of great significance to understand the potential mechanism of isoform-selectivity. Because of the important role of type I PI3K family members in various pathophysiological processes, isoform-selective PI3K inhibitors may ultimately have considerable efficacy in a wide range of human diseases. This review summarizes the progress of isoformselective PI3K inhibitors in preclinical and early clinical studies for anticancer and other various diseases.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alfredo Sierra-Cristancho ◽  
Luis González-Osuna ◽  
Daniela Poblete ◽  
Emilio A. Cafferata ◽  
Paola Carvajal ◽  
...  

AbstractThis study aimed to analyze the root anatomy and root canal system morphology of mandibular first premolars in a Chilean population. 186 teeth were scanned using micro-computed tomography and reconstructed three-dimensionally. The root canal system morphology was classified using both Vertucci’s and Ahmed’s criteria. The radicular grooves were categorized using the ASUDAS system, and the presence of Tomes’ anomalous root was associated with Ahmed’s score. A single root canal was identified in 65.05% of teeth, being configuration type I according to Vertucci’s criteria and code 1MP1 according to Ahmed’s criteria. Radicular grooves were observed in 39.25% of teeth. The ASUDAS scores for radicular grooves were 60.75%, 13.98%, 12.36%, 10.22%, 2.15%, and 0.54%, from grade 0 to grade 5, respectively. The presence of Tomes’ anomalous root was identified only in teeth with multiple root canals, and it was more frequently associated with code 1MP1–2 of Ahmed’s criteria. The root canal system morphology of mandibular first premolars showed a wide range of anatomical variations in the Chilean population. Teeth with multiple root canals had a higher incidence of radicular grooves, which were closely related to more complex internal anatomy. Only teeth with multiple root canals presented Tomes’ anomalous root.


Microbiome ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Dieter M. Tourlousse ◽  
Koji Narita ◽  
Takamasa Miura ◽  
Mitsuo Sakamoto ◽  
Akiko Ohashi ◽  
...  

Abstract Background Validation and standardization of methodologies for microbial community measurements by high-throughput sequencing are needed to support human microbiome research and its industrialization. This study set out to establish standards-based solutions to improve the accuracy and reproducibility of metagenomics-based microbiome profiling of human fecal samples. Results In the first phase, we performed a head-to-head comparison of a wide range of protocols for DNA extraction and sequencing library construction using defined mock communities, to identify performant protocols and pinpoint sources of inaccuracy in quantification. In the second phase, we validated performant protocols with respect to their variability of measurement results within a single laboratory (that is, intermediate precision) as well as interlaboratory transferability and reproducibility through an industry-based collaborative study. We further ascertained the performance of our recommended protocols in the context of a community-wide interlaboratory study (that is, the MOSAIC Standards Challenge). Finally, we defined performance metrics to provide best practice guidance for improving measurement consistency across methods and laboratories. Conclusions The validated protocols and methodological guidance for DNA extraction and library construction provided in this study expand current best practices for metagenomic analyses of human fecal microbiota. Uptake of our protocols and guidelines will improve the accuracy and comparability of metagenomics-based studies of the human microbiome, thereby facilitating development and commercialization of human microbiome-based products.


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