Exploration of multivariate analysis in microbial coding sequence modeling

Effect of chemotherapy on urinary volatile biomarkers for lung cancer by HS-SPME-GC-MS and chemometrics

BACKGROUND

Volatile organic compounds (VOCs) have been studied as possible biomarkers in several diseases, including lung cancer. Early detection of cancer can improve long-term survival rates and the quality of life, so the study of VOCs in exhaled breath and urine has been increasing in recent years. This study aimed to assess the urinary VOCs that are modified after chemotherapy to identify those with the potential to be lung cancer biomarkers that can be monitored during treatment.

METHODS

Three urine samples from 10 men with stage IV lung adenocarcinoma were collected, as well as urine samples from 14 men with other types of cancer (control group). All samples were analyzed by headspace-solid phase microextraction gas chromatography coupled with mass spectrometry.

RESULTS

A total of 21 urinary VOCs were found with different levels after the administration of chemotherapy, with 2-pentanone being one of those that significantly decreased. Furthermore, 2-pentanone and 3-hydroxy-2,4,4-trimethylpentyl-2-methylpropanoate showed statistically significant differences with the control group.

CONCLUSIONS

Chemotherapy administered to patients with advanced lung adenocarcinoma modified the volatile profile of urine. 2-Pentanone, a final product of the increased rate of fatty acid oxidation and protein hypermetabolism, significantly decreased after chemotherapy. Therefore, monitoring its urinary excretion could be very useful since its decrease over time could indicate an adequate response to chemotherapy and arrest of cancer development. Another VOC that could be a potential lung cancer biomarker is 3 hydroxy-2,4,4-trimethylpentyl-2-methylpropanoate, whose origin may be due to inhibition of the propanoic acid metabolic pathway or increased aldehyde dehydrogenase activity.

Feasibility of using volatile urine fingerprints for the differentiation of sexually transmitted infections

Sexually transmitted infections (STIs) are a public health problem worldwide, and current diagnostic methods have certain limitations. In recent years, volatile organic compounds (VOCs) have been studied as an alternative diagnostic method. Due to this, this study aimed to detect, in vaginal swabs and urine samples, VOCs emitted by highly prevalent STIs-causing bacteria (Chlamydia trachomatis, Mycoplasma genitalium, and Neisseria gonorrhoeae) to identify potential biomarkers that allow the detection of these STIs. VOCs detected in urine samples showed a better differentiation of patients with STIs due to C. trachomatis from those not infected, with 2,6-dimethyl-4-heptanone as the volatile compound most related to the presence of this bacterium. Among the VOCs most related to M. genitalium in urine, 4-methyltetradecane and 2-methylpentadecane stood out, while 3,4,4-trimethyl-2-cyclohexen-1-one was the VOC most closely related to N. gonorrhoeae infection. Moreover, C12 alcohols were the main VOC family associated with positive samples in all three bacteria, which could indicate the presence of aldehyde reductases in their metabolism. In contrast, alcohols such as 3-methyl-1-heptanol and 1-octanol, as well as dimethyl esters, were more associated with negative samples and may be useful in ruling out an STI caused by one of these three bacteria. In short, the VOCs identified as potential biomarkers in patients with infection by C. trachomatis, M. genitalium, or N. gonorrhoeae could be used in the early diagnosis of these STIs, quickly interrupting the chain of transmission, especially interesting in asymptomatic patients. KEY POINTS: • Sexually transmitted infections are a serious public health problem worldwide. • The study of VOCs in multiple infections is increasing in recent years. • The identification of volatile biomarkers could allow new diagnostic methods.

Comparing K-mer based methods for improved classification of 16S sequences

Background

The need for precise and stable taxonomic classification is highly relevant in modern microbiology. Parallel to the explosion in the amount of sequence data accessible, there has also been a shift in focus for classification methods. Previously, alignment-based methods were the most applicable tools. Now, methods based on counting K-mers by sliding windows are the most interesting classification approach with respect to both speed and accuracy. Here, we present a systematic comparison on five different K-mer based classification methods for the 16S rRNA gene. The methods differ from each other both in data usage and modelling strategies. We have based our study on the commonly known and well-used naïve Bayes classifier from the RDP project, and four other methods were implemented and tested on two different data sets, on full-length sequences as well as fragments of typical read-length.

Results

The difference in classification error obtained by the methods seemed to be small, but they were stable and for both data sets tested. The Preprocessed nearest-neighbour (PLSNN) method performed best for full-length 16S rRNA sequences, significantly better than the naïve Bayes RDP method. On fragmented sequences the naïve Bayes Multinomial method performed best, significantly better than all other methods. For both data sets explored, and on both full-length and fragmented sequences, all the five methods reached an error-plateau.

Conclusions

We conclude that no K-mer based method is universally best for classifying both full-length sequences and fragments (reads). All methods approach an error plateau indicating improved training data is needed to improve classification from here. Classification errors occur most frequent for genera with few sequences present. For improving the taxonomy and testing new classification methods, the need for a better and more universal and robust training data set is crucial.

Comparative metabolomics of muscle interstitium fluid in human trapezius myalgia: an in vivo microdialysis study

Purpose

The mechanisms behind trapezius myalgia are unclear. Many hypotheses have been presented suggesting an altered metabolism in the muscle. Here, muscle microdialysate from healthy and myalgic muscle is analysed using metabolomics. Metabolomics analyse a vast number of metabolites, enabling a comprehensive explorative screening of the cellular processes in the muscle.

Methods

Microdialysate samples were obtained from the shoulder muscle of healthy and myalgic subjects that performed a work and stress test. Samples from the baseline period and from the recovery period were analysed using gas chromatography—mass spectrometry (GC–MS) together with multivariate analysis to detect differences in extracellular content of metabolites between groups. Systematic differences in metabolites between groups were identified using multivariate analysis and orthogonal partial least square discriminate analysis (OPLS-DA). A complementary Mann–Whitney U test of group difference in individual metabolites was also performed.

Results

A large number of metabolites were detected and identified in this screening study. At baseline, no systematic differences between groups were observed according to the OPLS-DA. However, two metabolites, l-leucine and pyroglutamic acid, were significantly more abundant in the myalgic muscle compared to the healthy muscle. In the recovery period, systematic difference in metabolites between the groups was observed according to the OPLS-DA. The groups differed in amino acids, fatty acids and carbohydrates. Myristic acid and putrescine were significantly more abundant and beta-d-glucopyranose was significantly less abundant in the myalgic muscle.

Conclusion

This study provides important information regarding the metabolite content, thereby presenting new clues regarding the pathophysiology of the myalgic muscle.

A systematic search for discriminating sites in the 16S ribosomal RNA gene

Background

The 16S rRNA is by far the most common genomic marker used for prokaryotic classification, and has been used extensively in metagenomic studies over recent years. Along the 16S gene there are regions with more or less variation across the kingdom of bacteria. Nine variable regions have been identified, flanked by more conserved parts of the sequence. It has been stated that the discriminatory power of the 16S marker lies in these variable regions. In the present study we wanted to examine this more closely, and used a supervised learning method to search systematically for sites that contribute to correct classification at either the phylum or genus level.

Results

When classifying phyla the site selection algorithm located 50 discriminative sites. These were scattered over most of the alignments and only around half of them were located in the variable regions. The selected sites did, however, have an entropy significantly larger than expected, meaning they are sites of large variation. We found that the discriminative sites typically have a large entropy compared to their closest neighbours along the alignments. When classifying genera the site selection algorithm needed around 80% of the sites in the 16S gene before the classification error reached a minimum. This means that all variation, in both variable and conserved regions, is needed in order to separate genera.

Conclusions

Our findings does not support the statement that the discriminative power of the 16S gene is located only in the variable regions. Variable regions are important, but just as many discriminative sites are found in the more conserved parts. The discriminative power is typically found in sites of large variation located inside shorter regions of higher conservation.