Data-driven identification of plasma metabolite clusters and metabolites of interest for potential detection of early-stage non-small cell lung cancer cases versus cancer-free controls

BACKGROUND

Metabolomics is a potential means for biofluid-based lung cancer detection. We conducted a non-targeted, data-driven assessment of plasma from early-stage non-small cell lung cancer (ES-NSCLC) cases versus cancer-free controls (CFC) to explore and identify the classes of metabolites for further targeted metabolomics biomarker development.

METHODS

Plasma from 250 ES-NSCLC cases and 250 CFCs underwent ultra-high-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) in positive and negative electrospray ionization (ESI) modes. Molecular feature extraction, formula generation, and find-by-ion tools annotated metabolic entities. Analysis was restricted to endogenous metabolites present in ≥ 80% of samples. Unsupervised hierarchical cluster analysis identified clusters of metabolites. The metabolites with the strongest correlation with the principal component of each cluster were included in logistic regression modeling to assess discriminatory performance with and without adjustment for clinical covariates.

RESULTS

A total of 1900 UHPLC-QTOF-MS assessments identified 1667 and 2032 endogenous metabolites in the ESI-positive and ESI-negative modes, respectively. After data filtration, 676 metabolites remained, and 12 clusters of metabolites were identified from each ESI mode. Multivariable logistic regression using the representative metabolite from each cluster revealed effective classification of cases from controls with overall diagnostic accuracy of 91% (ESI positive) and 94% (ESI negative). Metabolites of interest identified for further targeted analysis include the following: 1b, 3a, 12a-trihydroxy-5b-cholanoic acid, pyridoxamine 5'-phosphate, sphinganine 1-phosphate, gamma-CEHC, 20-carboxy-leukotriene B4, isodesmosine, and 18-hydroxycortisol.

CONCLUSIONS

Plasma-based metabolomic detection of early-stage NSCLC appears feasible. Further metabolomics studies targeting phospholipid, steroid, and fatty acid metabolism are warranted to further develop noninvasive metabolomics-based detection of early-stage NSCLC.

GPTransformer: A Transformer-Based Deep Learning Method for Predicting Fusarium Related Traits in Barley

Fusarium head blight (FHB) incited by Fusarium graminearum Schwabe is a devastating disease of barley and other cereal crops worldwide. Fusarium head blight is associated with trichothecene mycotoxins such as deoxynivalenol (DON), which contaminates grains, making them unfit for malting or animal feed industries. While genetically resistant cultivars offer the best economic and environmentally responsible means to mitigate disease, parent lines with adequate resistance are limited in barley. Resistance breeding based upon quantitative genetic gains has been slow to date, due to intensive labor requirements of disease nurseries. The production of a high-throughput genome-wide molecular marker assembly for barley permits use in development of genomic prediction models for traits of economic importance to this crop. A diverse panel consisting of 400 two-row spring barley lines was assembled to focus on Canadian barley breeding programs. The panel was evaluated for FHB and DON content in three environments and over 2 years. Moreover, it was genotyped using an Illumina Infinium High-Throughput Screening (HTS) iSelect custom beadchip array of single nucleotide polymorphic molecular markers (50 K SNP), where over 23 K molecular markers were polymorphic. Genomic prediction has been demonstrated to successfully reduce FHB and DON content in cereals using various statistical models. Herein, we have studied an alternative method based on machine learning and compare it with a statistical approach. The bi-allelic SNPs represented pairs of alleles and were encoded in two ways: as categorical (-1, 0, 1) or using Hardy-Weinberg probability frequencies. This was followed by selecting essential genomic markers for phenotype prediction. Subsequently, a Transformer-based deep learning algorithm was applied to predict FHB and DON. Apart from the Transformer method, a Residual Fully Connected Neural Network (RFCNN) was also applied. Pearson correlation coefficients were calculated to compare true vs. predicted outputs. Models which included all markers generally showed marginal improvement in prediction. Hardy-Weinberg encoding generally improved correlation for FHB (6.9%) and DON (9.6%) for the Transformer network. This study suggests the potential of the Transformer based method as an alternative to the popular BLUP model for genomic prediction of complex traits such as FHB or DON, having performed equally or better than existing machine learning and statistical methods.

Continent-wide effects of urbanization on bird and mammal genetic diversity

Urbanization and associated environmental changes are causing global declines in vertebrate populations. In general, population declines of the magnitudes now detected should lead to reduced effective population sizes for animals living in proximity to humans and disturbed lands. This is a cause for concern because effective population sizes set the rate of genetic diversity loss due to genetic drift, the rate of increase in inbreeding and the efficiency with which selection can act on beneficial alleles. We predicted that the effects of urbanization should decrease effective population size and genetic diversity, and increase population-level genetic differentiation. To test for such patterns, we repurposed and reanalysed publicly archived genetic datasets for North American birds and mammals. After filtering, we had usable raw genotype data from 85 studies and 41 023 individuals, sampled from 1008 locations spanning 41 mammal and 25 bird species. We used census-based urban-rural designations, human population density and the Human Footprint Index as measures of urbanization and habitat disturbance. As predicted, mammals sampled in more disturbed environments had lower effective population sizes and genetic diversity, and were more genetically differentiated from those in more natural environments. There were no consistent relationships detectable for birds. This suggests that, in general, mammal populations living near humans may have less capacity to respond adaptively to further environmental changes, and be more likely to suffer from effects of inbreeding.

MicroRNA-guided regulation of heat stress response in wheat

Background

With rising global temperature, understanding plants’ adaptation to heat stress has implications in plant breeding. MicroRNAs (miRNAs) are small, non-coding, regulatory RNAs guiding gene expression at the post-transcriptional level. In this study, small RNAs and the degradome (parallel analysis of RNA ends) of leaf tissues collected from control and heat-stressed wheat plants immediately at the end of the stress period and 1 and 4 days later were analysed.

Results

Sequencing of 24 small RNA libraries produced 55.2 M reads while 404 M reads were obtained from the corresponding 24 PARE libraries. From these, 202 miRNAs were ascertained, of which mature miRNA evidence was obtained for 104 and 36 were found to be differentially expressed after heat stress. The PARE analysis identified 589 transcripts targeted by 84 of the ascertained miRNAs. PARE sequencing validated the targets of the conserved members of miRNA156, miR166 and miR393 families as squamosa promoter-binding-like, homeobox leucine-zipper and transport inhibitor responsive proteins, respectively. Heat stress responsive miRNA targeted superoxide dismutases and an array of homeobox leucine-zipper proteins, F-box proteins and protein kinases. Query of miRNA targets to interactome databases revealed a predominant association of stress responses such as signalling, antioxidant activity and ubiquitination to superoxide dismutases, F-box proteins, pentatricopeptide repeat-containing proteins and mitochondrial transcription termination factor-like proteins.

Conclusion

The interlaced data set generated in this study identified and validated heat stress regulated miRNAs and their target genes associated with thermotolerance. Such accurate identification and validation of miRNAs and their target genes are essential to develop novel regulatory gene-based breeding strategies.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5799-6) contains supplementary material, which is available to authorized users.

Neptune: a bioinformatics tool for rapid discovery of genomic variation in bacterial populations

The ready availability of vast amounts of genomic sequence data has created the need to rethink comparative genomics algorithms using 'big data' approaches. Neptune is an efficient system for rapidly locating differentially abundant genomic content in bacterial populations using an exact k-mer matching strategy, while accommodating k-mer mismatches. Neptune's loci discovery process identifies sequences that are sufficiently common to a group of target sequences and sufficiently absent from non-targets using probabilistic models. Neptune uses parallel computing to efficiently identify and extract these loci from draft genome assemblies without requiring multiple sequence alignments or other computationally expensive comparative sequence analyses. Tests on simulated and real datasets showed that Neptune rapidly identifies regions that are both sensitive and specific. We demonstrate that this system can identify trait-specific loci from different bacterial lineages. Neptune is broadly applicable for comparative bacterial analyses, yet will particularly benefit pathogenomic applications, owing to efficient and sensitive discovery of differentially abundant genomic loci. The software is available for download at: http://github.com/phac-nml/neptune.

Deep sequencing of wheat sRNA transcriptome reveals distinct temporal expression pattern of miRNAs in response to heat, light and UV

Understanding of plant adaptation to abiotic stresses has implications in plant breeding, especially in the context of climate change. MicroRNAs (miRNAs) and short interfering RNAs play a crucial role in gene regulation. Here, wheat plants were exposed to one of the following stresses: continuous light, heat or ultraviolet radiations over five consecutive days and leaf tissues from three biological replicates were harvested at 0, 1, 2, 3, 7 and 10 days after treatment (DAT). A total of 72 small RNA libraries were sequenced on the Illumina platform generating ~524 million reads corresponding to ~129 million distinct tags from which 232 conserved miRNAs were identified. The expression levels of 1, 2 and 79 miRNAs were affected by ultraviolet radiation, continuous light and heat, respectively. Approximately 55% of the differentially expressed miRNAs were downregulated at 0 and 1 DAT including miR398, miR528 and miR156 that control mRNAs involved in activation of signal transduction pathways and flowering. Other putative targets included histone variants and methyltransferases. These results suggest a temporal miRNA-guided post-transcriptional regulation that enables wheat to respond to abiotic stresses, particularly heat. Designing novel wheat breeding strategies such as regulatory gene-based marker assisted selection depends on accurate identification of stress induced miRNAs.

Metabolic network prediction through pairwise rational kernels

BACKGROUND

Metabolic networks are represented by the set of metabolic pathways. Metabolic pathways are a series of biochemical reactions, in which the product (output) from one reaction serves as the substrate (input) to another reaction. Many pathways remain incompletely characterized. One of the major challenges of computational biology is to obtain better models of metabolic pathways. Existing models are dependent on the annotation of the genes. This propagates error accumulation when the pathways are predicted by incorrectly annotated genes. Pairwise classification methods are supervised learning methods used to classify new pair of entities. Some of these classification methods, e.g., Pairwise Support Vector Machines (SVMs), use pairwise kernels. Pairwise kernels describe similarity measures between two pairs of entities. Using pairwise kernels to handle sequence data requires long processing times and large storage. Rational kernels are kernels based on weighted finite-state transducers that represent similarity measures between sequences or automata. They have been effectively used in problems that handle large amount of sequence information such as protein essentiality, natural language processing and machine translations.

RESULTS

We create a new family of pairwise kernels using weighted finite-state transducers (called Pairwise Rational Kernel (PRK)) to predict metabolic pathways from a variety of biological data. PRKs take advantage of the simpler representations and faster algorithms of transducers. Because raw sequence data can be used, the predictor model avoids the errors introduced by incorrect gene annotations. We then developed several experiments with PRKs and Pairwise SVM to validate our methods using the metabolic network of Saccharomyces cerevisiae. As a result, when PRKs are used, our method executes faster in comparison with other pairwise kernels. Also, when we use PRKs combined with other simple kernels that include evolutionary information, the accuracy values have been improved, while maintaining lower construction and execution times.

CONCLUSIONS

The power of using kernels is that almost any sort of data can be represented using kernels. Therefore, completely disparate types of data can be combined to add power to kernel-based machine learning methods. When we compared our proposal using PRKs with other similar kernel, the execution times were decreased, with no compromise of accuracy. We also proved that by combining PRKs with other kernels that include evolutionary information, the accuracy can also also be improved. As our proposal can use any type of sequence data, genes do not need to be properly annotated, avoiding accumulation errors because of incorrect previous annotations.