How to distinguish healthy from diseased? Classification strategy for mass spectrometry‐based clinical proteomics

SELDI-TOF-MS is rapidly gaining popularity as a screening tool for clinical applications of proteomics. Application of adequate statistical techniques in all the stages from measurement to information is obligatory. One of the statistical methods often used in proteomics is classification: the assignment of subjects to discrete categories, for example healthy or diseased. Lately, many new classification methods have been developed, often specifically for the analysis of X-omics data. For proteomics studies a good strategy for evaluating classification results is of prime importance, because usually the number of objects will be small and it would be wasteful to set aside part of these as a 'mere' test set. The present paper offers such a strategy in the form of a protocol which can be used for choosing among different statistical classification methods and obtaining figures of merit of their performance. This paper also illustrates the usefulness of proteomics in a clinical setting, serum samples from Gaucher disease patients, when used in combination with an appropriate classification method.     

Statistical validation of megavariate effects in ASCA

Background  

Innovative extensions of (M) ANOVA gain common ground for the analysis of designed metabolomics experiments. ASCA is such a multivariate analysis method; it has successfully estimated effects in megavariate metabolomics data from biological experiments. However, rigorous statistical validation of megavariate effects is still problematic because megavariate extensions of the classical F-test do not exist.     

Pattern of Resource Allocation of Six Plantago Species with Different Breeding Systems

Plantago exhibit great deal of differences in the breeding system. The reproductive effort calculated on the basis of, (i) dry biomass of foliar and floral parts and (ii) seed output-weight (mg) per unit leaf area (cm²), exhibits relation with breeding system. The predominantly inbreeding taxa invest higher reproductive effort compared to their outbreeding allies. In terms of sex allocation strategies, the outbreeding species like P. lanceolata, P. lagopus invest more to the development of floral features and to male functions. On the contrary, inbreeding species such as P. patagonica, P. drummondii, and P. ovata invest greater resources to the female function. Received 13 April 1998/ Accepted in revised form 6 November 1998     

Assessment of PLSDA cross validation

Classifying groups of individuals based on their metabolic profile is one of the main topics in metabolomics research. Due to the low number of individuals compared to the large number of variables, this is not an easy task. PLSDA is one of the data analysis methods used for the classification. Unfortunately this method eagerly overfits the data and rigorous validation is necessary. The validation however is far from straightforward. Is this paper we will discuss a strategy based on cross model validation and permutation testing to validate the classification models. It is also shown that too optimistic results are obtained when the validation is not done properly. Furthermore, we advocate against the use of PLSDA score plots for inference of class differences.     

A classification model for the Leiden proteomics competition

A strategy is presented to build a discrimination model in proteomics studies. The model is built using cross-validation. This cross-validation step can simply be combined with a variable selection method, called rank products. The strategy is especially suitable for the low-samples-to-variables-ratio (undersampling) case, as is often encountered in proteomics and metabolomics studies. As a classification method, Principal Component Discriminant Analysis is used; however, the methodology can be used with any classifier. A data set containing serum samples from breast cancer patients and healthy controls is analysed. Double cross-validation shows that the sensitivity of the model is 82% and the specificity 86%. Potential putative biomarkers are identified using the variable selection method. In each cross-validation loop a classification model is built. The final classification uses a majority voting scheme from the ensemble classifier.     

Biology and interactions of two distinct monopartite begomoviruses and betasatellites associated with radish leaf curl disease in India

Background

Emerging whitefly transmitted begomoviruses are major pathogens of vegetable and fibre crops throughout the world, particularly in tropical and sub-tropical regions. Mutation, pseudorecombination and recombination are driving forces for the emergence and evolution of new crop-infecting begomoviruses. Leaf curl disease of field grown radish plants was noticed in Varanasi and Pataudi region of northern India. We have identified and characterized two distinct monopartite begomoviruses and associated beta satellite DNA causing leaf curl disease of radish (Raphanus sativus) in India.

Results

We demonstrate that RaLCD is caused by a complex of two Old World begomoviruses and their associated betasatellites. Radish leaf curl virus-Varanasi is identified as a new recombinant species, Radish leaf curl virus (RaLCV) sharing maximum nucleotide identity of 87.7% with Tomato leaf curl Bangladesh virus-[Bangladesh:2] (Accession number AF188481) while the virus causing radish leaf curl disease-Pataudi is an isolate of Croton yellow vein mosaic virus-[India] (CYVMV-IN) (Accession number AJ507777) sharing 95.8% nucleotide identity. Further, RDP analysis revealed that the RaLCV has a hybrid genome, a putative recombinant between Euphorbia leaf curl virus and Papaya leaf curl virus. Cloned DNA of either RaLCV or CYVMV induced mild leaf curl symptoms in radish plants. However, when these clones (RaLCV or CYVMV) were individually co-inoculated with their associated cloned DNA betasatellite, symptom severity and viral DNA levels were increased in radish plants and induced typical RaLCD symptoms. To further extend these studies, we carried out an investigation of the interaction of these radish-infecting begomoviruses and their associated satellite, with two tomato infecting begomoviruses (Tomato leaf curl Gujarat virus and Tomato leaf curl New Delhi virus). Both of the tomato-infecting begomoviruses showed a contrasting and differential interaction with DNA satellites, not only in the capacity to interact with these molecules but also in the modulation of symptom phenotypes by the satellites.

Conclusion

This is the first report and experimental demonstration of Koch's postulate for begomoviruses associated with radish leaf curl disease. Further observations also provide direct evidence of lateral movement of weed infecting begomovirus in the cultivated crops and the present study also suggests that the exchange of betasatellites with other begomoviruses would create a new disease complex posing a serious threat to crop production.     

Mucin secretion by the human colon cell line LS174T is regulated by bile salts

We recently reported that bile salts play a role in the regulation of mucin secretion by cultured dog gallbladder epithelial cells. In this study we have examined whether bile salts also influence mucin secretion by the human epithelial colon cell line LS174T. Solutions of bile salts were applied to monolayers of LS174T cells. Mucin secretion was quantified by measuring the secretion of [3H]GlcNAc labeled glycoproteins. Both unconjugated bile salts as well as taurine conjugated bile salts stimulated mucin secretion by the colon cells in a dose-dependent fashion. Hydrophobic bile salts were more potent stimulators than hydrophilic bile salts. Free (unconjugated) bile salts were more stimulatory compared with their taurine conjugated counterparts. Stimulation of mucin secretion by LS174T cells was found to occur at much lower bile salt concentrations than in the experiments with the dog gallbladder epithelial cells. The protein kinase C activators PMA and PDB had no stimulatory effect on mucin secretion. We conclude that mucin secretion by the human colon epithelial cell line LS174T is regulated by bile salts. We suggest that regulation of mucin secretion by bile salts might be a common mechanism, by which different epithelia protect themselves against the detergent action of bile salts, to which they are exposed throughout the gastrointestinal tract.