Statistical Cluster Analysis of the British Thoracic Society Severe Refractory Asthma Registry: Clinical Outcomes and Phenotype Stability

Background

Severe refractory asthma is a heterogeneous disease. We sought to determine statistical clusters from the British Thoracic Society Severe refractory Asthma Registry and to examine cluster-specific outcomes and stability.

Methods

Factor analysis and statistical cluster modelling was undertaken to determine the number of clusters and their membership (N = 349). Cluster-specific outcomes were assessed after a median follow-up of 3 years. A classifier was programmed to determine cluster stability and was validated in an independent cohort of new patients recruited to the registry (n = 245).

Findings

Five clusters were identified. Cluster 1 (34%) were atopic with early onset disease, cluster 2 (21%) were obese with late onset disease, cluster 3 (15%) had the least severe disease, cluster 4 (15%) were the eosinophilic with late onset disease and cluster 5 (15%) had significant fixed airflow obstruction. At follow-up, the proportion of subjects treated with oral corticosteroids increased in all groups with an increase in body mass index. Exacerbation frequency decreased significantly in clusters 1, 2 and 4 and was associated with a significant fall in the peripheral blood eosinophil count in clusters 2 and 4. Stability of cluster membership at follow-up was 52% for the whole group with stability being best in cluster 2 (71%) and worst in cluster 4 (25%). In an independent validation cohort, the classifier identified the same 5 clusters with similar patient distribution and characteristics.

Interpretation

Statistical cluster analysis can identify distinct phenotypes with specific outcomes. Cluster membership can be determined using a classifier, but when treatment is optimised, cluster stability is poor.     

Marginal analysis of multiple outcomes with informative cluster size

In surveillance studies of periodontal disease, the relationship between disease and other health and socioeconomic conditions is of key interest. To determine whether a patient has periodontal disease, multiple clinical measurements (eg, clinical attachment loss, alveolar bone loss, and tooth mobility) are taken at the tooth‐level. Researchers often create a composite outcome from these measurements or analyze each outcome separately. Moreover, patients have varying number of teeth, with those who are more prone to the disease having fewer teeth compared to those with good oral health. Such dependence between the outcome of interest and cluster size (number of teeth) is called informative cluster size and results obtained from fitting conventional marginal models can be biased. We propose a novel method to jointly analyze multiple correlated binary outcomes for clustered data with informative cluster size using the class of generalized estimating equations (GEE) with cluster‐specific weights. We compare our proposed multivariate outcome cluster‐weighted GEE results to those from the convectional GEE using the baseline data from Veterans Affairs Dental Longitudinal Study. In an extensive simulation study, we show that our proposed method yields estimates with minimal relative biases and excellent coverage probabilities.     

Cluster analysis and disease mapping--why,when, and how? A step by step guide.

Growing public awareness of environmental hazards has led to an increased demand for public health authorities to investigate geographical clustering of diseases. Although such cluster analysis is nearly always ineffective in identifying causes of disease, it often has to be used to address public concern about environmental hazards. Interpreting the resulting data is not straightforward, however, and this paper presents a guide for the non-specialist. The pitfalls include the fact that cluster analyses are usually done post hoc, and not as a result of a prior hypothesis. This is particularly true for investigations prompted by reported clusters, which have the inherent danger of overestimating the disease rate through "boundary shrinkage" of the population from which the cases are assumed to have arisen. In disease surveillance the problem of making multiple comparisons can be overcome by testing for clustering and autocorrelation. When rates of disease are illustrated in disease maps undue focus on areas where random fluctuation is greatest can be minimised by smoothing techniques. Despite the fact that cluster analyses rarely prove fruitful in identifying causation, they may-like single case reports-have the potential to generate new knowledge.     

miR-106-363基因簇在脊椎动物中的进化分析

microRNA(miRNA)是一类在真核生物体内广泛表达的非编码小分子RNA,在生物体生长、发育以及疾病发生等过程中都起着极其重要的作用。miR-106-363基因簇是一个高度保守的基因簇,编码miR-106、miR-18、miR-20、miR-19、miR-92和miR-363等6个miRNA。本文通过对miRBase数据库检索以及同源搜索的方法在16种脊椎动物中搜索到了miR-106-363基因簇。该miRNA基因簇在高等脊椎动物中高度保守,稳定存在。系统进化树分析表明,miR-106-363基因簇与miR-17-92基因簇有着共同的祖先,且在进化上miR-17-92基因簇有着更早的起源。     

Variability within Clones of Potato cv. Russet Burbank to Infection and Severity of Common Scab Disease of Potato

Significant variability in the level of resistance to common scab disease (to both disease incidence – tuber surface area affected, and severity – lesion depth) within potato ( Solanum tuberosum ) cv. Russet Burbank clonal lines and other selected processing cultivars was shown. Across two trials, cvs. Russet Nugget and Shepody and Russet Burbank clone British Columbia were consistently grouped within the cluster of least disease incidence whereas cv. Russet Norkotah and Vancouver unit four were grouped within the cluster showing greatest disease incidence. Russet Nugget and British Columbia were also consistently grouped within the cluster showing least disease severity, whereas Shepody and Russet Burbank clone Starks were grouped within the cluster with greatest severity scores. For some clones, consistency of rating across trials was not always apparent. Of note for the Tasmanian industry, there were differences within the five commercial Vancouver clonal units used interchangeably, with unit 4 showing significantly greater disease severity and incidence than some of the other units. However the extent of increased disease never exceeded 1.75 times the best of the other Vancouver units and the relevance of these results to the relative field performance of unit 4 remains to be tested.     

Transmission and dynamics of tuberculosis on generalized households

Tuberculosis (TB) transmission is enhanced by systematic exposure to an infectious individual. This enhancement usually takes place at either the home, workplace, and/or school (generalized household). Typical epidemiological models do not incorporate the impact of generalized households on the study of disease dynamics. Models that incorporate cluster (generalized household) effects and focus on their impact on TB's transmission dynamics are developed. Detailed models that consider the effect of casual infections, that is, those generated outside a cluster, are also presented. We find expressions for the Basic Reproductive Number as a function of cluster size. The formula for R0 separates the contributions of cluster and casual infections in the generation of secondary TB infections. Relationships between cluster and classical epidemic models are discussed as well as the concept of critical cluster size.     

The interleukin 1 gene cluster contains a major susceptibility locus for ankylosing spondylitis

Ankylosing spondylitis (AS) is a common and highly heritable inflammatory arthropathy. Although the gene HLA-B27 is almost essential for the inheritance of the condition, it alone is not sufficient to explain the pattern of familial recurrence of the disease. We have previously demonstrated suggestive linkage of AS to chromosome 2q13, a region containing the interleukin 1 (IL-1) family gene cluster, which includes several strong candidates for involvement in the disease. In the current study, we describe strong association and transmission of IL-1 family gene cluster single-nucleotide polymorphisms and haplotypes with AS.     

Relationships between members of the Mycoplasma mycoides cluster as shown by DNA probes and sequence analysis.

A gene probe, CAP-21, which demonstrated interrelationships between the members of the Mycoplasma mycoides cluster was developed. The probe easily differentiated mycoplasmas in this cluster by clear and predictable hybridization patterns in Southern blots and separated the cluster into four groups. Strains of M. mycoides subsp. mycoides which were capable of causing contagious bovine pleuropneumonia composed one group. Strains of M. mycoides subsp. mycoides which did not cause contagious bovine pleuropneumonia together with strains of M. mycoides subsp. capri composed the second group. Mycoplasma capricolum and the F38 mycoplasmas formed a third group, while the bovine group 7 mycoplasmas composed a separate, fourth group. Further support for the above grouping of the cluster was obtained when amplified DNA analogous to the probe from one representative strain of each of the cluster members was sequenced and these data were used to construct a phylogenic tree. Contagious caprine pleuropneumonia is recognized as an important disease, and the etiological agent of this disease is now known to be the F38 mycoplasma. The CAP-21 probe did not differentiate between M. capricolum and the closely related F38 mycoplasma. A second probe, F38-12, which was capable of distinguishing these two mycoplasmas was made.     

Frataxin and Mitochondrial FeS Cluster Biogenesis

Friedreich ataxia is an inherited neurodegenerative disease caused by frataxin deficiency. Frataxin is a conserved mitochondrial protein that plays a role in FeS cluster assembly in mitochondria. FeS clusters are modular cofactors that perform essential functions throughout the cell. They are synthesized by a multistep and multisubunit mitochondrial machinery that includes the scaffold protein Isu for assembling a protein-bound FeS cluster intermediate. Frataxin interacts with Isu, iron, and the cysteine desulfurase Nfs1, which supplies sulfide, thus placing it at the center of mitochondrial FeS cluster biosynthesis.     

A spatial scan statistic for multiple clusters

Spatial scan statistics are commonly used for geographical disease surveillance and cluster detection. While there are multiple clusters coexisting in the study area, they become difficult to detect because of clusters’ shadowing effect to each other. The recently proposed sequential method showed its better power for detecting the second weaker cluster, but did not improve the ability of detecting the first stronger cluster which is more important than the second one. We propose a new extension of the spatial scan statistic which could be used to detect multiple clusters. Through constructing two or more clusters in the alternative hypothesis, our proposed method accounts for other coexisting clusters in the detecting and evaluating process. The performance of the proposed method is compared to the sequential method through an intensive simulation study, in which our proposed method shows better power in terms of both rejecting the null hypothesis and accurately detecting the coexisting clusters. In the real study of hand-foot-mouth disease data in Pingdu city, a true cluster town is successfully detected by our proposed method, which cannot be evaluated to be statistically significant by the standard method due to another cluster’s shadowing effect.     

Likelihood Methods for Binary Responses of Present Components in a Cluster

Summary In some biomedical studies involving clustered binary responses (say, disease status), the cluster sizes can vary because some components of the cluster can be absent. When both the presence of a cluster component as well as the binary disease status of a present component are treated as responses of interest, we propose a novel two‐stage random effects logistic regression framework. For the ease of interpretation of regression effects, both the marginal probability of presence/absence of a component as well as the conditional probability of disease status of a present component, preserve the approximate logistic regression forms. We present a maximum likelihood method of estimation implementable using standard statistical software. We compare our models and the physical interpretation of regression effects with competing methods from literature. We also present a simulation study to assess the robustness of our procedure to wrong specification of the random effects distribution and to compare finite‐sample performances of estimates with existing methods. The methodology is illustrated via analyzing a study of the periodontal health status in a diabetic Gullah population.     

Mitochondrial targeting of human NADH dehydrogenase (ubiquinone) flavoprotein 2 (NDUFV2) and its association with early-onset hypertrophic cardiomyopathy and encephalopathy

Background  

NADH dehydrogenase (ubiquinone) flavoprotein 2 (NDUFV2), containing one iron sulfur cluster ([2Fe-2S] binuclear cluster N1a), is one of the core nuclear-encoded subunits existing in human mitochondrial complex I. Defects in this subunit have been associated with Parkinson's disease, Alzheimer's disease, Bipolar disorder, and Schizophrenia. The aim of this study is to examine the mitochondrial targeting of NDUFV2 and dissect the pathogenetic mechanism of one human deletion mutation present in patients with early-onset hypertrophic cardiomyopathy and encephalopathy.     

Application of Adaptive Cluster Sampling with a Data‐Driven Stopping Rule to Plant Disease Incidence

Plant pathologists need to manage plant diseases at low incidence levels. This needs to be performed efficiently in terms of precision, cost and time because most plant infections spread rapidly to other plants. Adaptive cluster sampling with a data‐driven stopping rule (ACS*) was proposed to control the final sample size and improve efficiency of the ordinary adaptive cluster sampling (ACS) when prior knowledge of population structure is not known. This study seeks to apply the ACS* design to plant diseases at various levels of clustering and incidences levels. Results from simulation study show that the ACS* is as efficient as the ordinary ACS design at low levels of disease incidence with highly clustered diseased plants and is an efficient design compared with simple random sampling (SRS) and ordinary ACS for some highly to less clustered diseased plants with moderate to higher levels of disease incidence.     

Identification of gene signatures from RNA-seq data using Pareto-optimal cluster algorithm

Background

Gene signatures are important to represent the molecular changes in the disease genomes or the cells in specific conditions, and have been often used to separate samples into different groups for better research or clinical treatment. While many methods and applications have been available in literature, there still lack powerful ones that can take account of the complex data and detect the most informative signatures.

Methods

In this article, we present a new framework for identifying gene signatures using Pareto-optimal cluster size identification for RNA-seq data. We first performed pre-filtering steps and normalization, then utilized the empirical Bayes test in Limma package to identify the differentially expressed genes (DEGs). Next, we used a multi-objective optimization technique, “Multi-objective optimization for collecting cluster alternatives” (MOCCA in R package) on these DEGs to find Pareto-optimal cluster size, and then applied k-means clustering to the RNA-seq data based on the optimal cluster size. The best cluster was obtained through computing the average Spearman’s Correlation Score among all the genes in pair-wise manner belonging to the module. The best cluster is treated as the signature for the respective disease or cellular condition.

Results

We applied our framework to a cervical cancer RNA-seq dataset, which included 253 squamous cell carcinoma (SCC) samples and 22 adenocarcinoma (ADENO) samples. We identified a total of 582 DEGs by Limma analysis of SCC versus ADENO samples. Among them, 260 are up-regulated genes and 322 are down-regulated genes. Using MOCCA, we obtained seven Pareto-optimal clusters. The best cluster has a total of 35 DEGs consisting of all-upregulated genes. For validation, we ran PAMR (prediction analysis for microarrays) classifier on the selected best cluster, and assessed the classification performance. Our evaluation, measured by sensitivity, specificity, precision, and accuracy, showed high confidence.

Conclusions

Our framework identified a multi-objective based cluster that is treated as a signature that can classify the disease and control group of samples with higher classification performance (accuracy 0.935) for the corresponding disease. Our method is useful to find signature for any RNA-seq or microarray data.

     

Outbreak of Rocky Mountain spotted fever in Córdoba, Colombia

Rocky Mountain spotted fever (RMSF) is a tick-borne disease caused by the obligate intracellular bacterium Rickettsia rickettsii. Although RMSF was first reported in Colombia in 1937, it remains a neglected disease. Herein, we describe the investigation of a large cluster of cases of spotted fever rickettsiosis in a new area of Colombia.     

The gene causing familial hypoalphalipoproteinemia is not caused by a defect in the apo AI-CIII-AIV gene cluster in a Spanish family

Summary High-density lipoprotein (HDL) cholesterol levels have an inverse relationship with the frequency of coronary and cerebrovascular disease. Most commonly HDL deficiency is environmentally modulated. Familial hypoalphalipoproteinemia (FHA) is a genetically determined HDL deficiency disease, in all likelihood transmitted as an autosomal dominant trait and associated with premature atherosclerosis. Apolipoprotein AI (apo AI) is the major apoprotein in the HDL particle, and defects in this protein have been suggested as the cause of FHA. We have identified a large family of Spanish descent with FHA and performed genetic linkage analysis using restriction fragment length polymorphisms in the Apo AI-CIII-AIV gene cluster to test this hypothesis. Results in this family formally exclude the apo AI-CIII-AIV gene cluster as the site for the mutation underlying FHA.     

MiR-183基因簇对动物感觉器官功能和发育及肿瘤发生的调控

MicroRNAs (miRNAs) 是一类长度约为22 nt的内源性非编码小RNA. 它们在后生动物基因组中普遍存在,通过抑制靶基因mRNA的翻译或将其降解,在转录后水平调控基因的表达. 越来越多的证据表明,miRNAs在动物发育和人类疾病发生中发挥重要作用. miR-183基因簇在后口动物和原口动物中高度保守,编码miR-182、miR-96和miR-183. miR-183基因簇在动物感觉器官中特异性表达,对动物感觉器官的发育和功能至关重要. miR-183基因簇还与人类的肺癌、肝癌、乳腺癌、胰腺癌和黑色素瘤等多种癌症相关. miR-183基因簇在多种肿瘤细胞中异常表达,它们通过调控与肿瘤细胞分裂和死亡相关基因,而起到促进或抑制肿瘤发生的作用. 本文对miR-183基因簇miRNAs在动物感觉器官功能和发育及人类肿瘤发生中的作用进行论述.     

Iron-sulfur cluster biogenesis and human disease

Iron-sulfur (Fe-S) clusters are essential for numerous biological processes, including mitochondrial respiratory chain activity and various other enzymatic and regulatory functions. Human Fe-S cluster assembly proteins are frequently encoded by single genes, and inherited defects in some of these genes cause disease. Recently, the spectrum of diseases attributable to abnormal Fe-S cluster biogenesis has extended beyond Friedreich ataxia to include a sideroblastic anemia with deficiency of glutaredoxin 5 and a myopathy associated with a deficiency of a Fe-S cluster assembly scaffold protein, ISCU. Mutations within other mammalian Fe-S cluster assembly genes could be causative for human diseases that manifest distinctive combinations of tissue-specific impairments. Thus, defects in the iron-sulfur cluster biogenesis pathway could underlie many human diseases.     

谷氧还蛋白2与人类健康

谷氧还蛋白2(Glutaredoxin 2,GLRX2)是一种相对分子质量较小的氧化还原酶,属于硫氧还蛋白家族成员,以谷胱甘肽为辅基调节细胞的氧化还原内环境。在非应激条件下,GLRX2结合铁硫簇,以二聚体形式存在,可能参与铁硫簇的转运或运输;当氧化压力增加时,铁硫簇解聚,GLRX2二聚体转化为GLRX2单体,利用单巯基或双巯基机制,发挥抗氧化应激和抗细胞凋亡的功能。GLRX2与人类健康和疾病,如心血管疾病、神经退行性疾病、白内障、肿瘤细胞生长与分化和精子成熟等密切相关。因此,对GLRX2的深入研究将有助于设计针对氧化应激的药物,为治疗和预防由此产生的疾病或健康问题带来新的希望。