Challenges in Lipidomics Biomarker Identification: Avoiding the Pitfalls and Improving Reproducibility.

Identification of features with high levels of confidence in liquid chromatography-mass spectrometry (LC-MS) lipidomics research is an essential part of biomarker discovery, but existing software platforms can give inconsistent results, even from identical spectral data. This poses a clear challenge for reproducibility in biomarker identification. In this work, we illustrate the reproducibility gap for two open-access lipidomics platforms, MS DIAL and Lipostar, finding just 14.0% identification agreement when analyzing identical LC-MS spectra using default settings. Whilst the software platforms performed more consistently using fragmentation data, agreement was still only 36.1% for MS2 spectra. This highlights the critical importance of validation across positive and negative LC-MS modes, as well as the manual curation of spectra and lipidomics software outputs, in order to reduce identification errors caused by closely related lipids and co-elution issues. This curation process can be supplemented by data-driven outlier detection in assessing spectral outputs, which is demonstrated here using a novel machine learning approach based on support vector machine regression combined with leave-one-out cross-validation. These steps are essential to reduce the frequency of false positive identifications and close the reproducibility gap, including between software platforms, which, for downstream users such as bioinformaticians and clinicians, can be an underappreciated source of biomarker identification errors.

Determinants of melon farmers' adaptation strategies to climate change hazards in south‒south Nigeria.

The constant changes experienced in agricultural activities due to climate change pose a great challenge to melon production. Hence, this research examined the determinants of melon farmers' adaptation strategies to cope with climate change hazards in southern-southern Nigeria. The research ultimately depended on primary data collected by using a set of questionnaires and interviews. The data were obtained from 260 samples retrieved from melon farmers by using multistage sampling techniques. The data were analyzed using the multivariate probit (MVP) model and partial eta squared test. The results of the MVP model showed that age (- 0.009), marital status (0.200), access to information on climate change (0.567) and crop insurance (0.214) were significant at the 0.01 level, while household size (- 0.030) was significant at the 0.05 level and determined the adoption of crop diversification. Educational level (0.012), extension contact (0.138) and access to credit (0.122) were significant at the 0.05 level, while access to information on climate change (0.415) was significant at the 0.01 level and determined the adoption of change in planting dates. Age (- 0.010) and access to information on climate change (0.381) were significant at the 0.01 level, while sex (- 0.139), marital status (0.158) and off-farm income (- 2.3E-7) were significant at the 0.05 level and determined the adoption of mixed farming. Farming experience (0.005) is significant at the 0.05 level, while access to information on climate change (0.529) and crop insurance (0.272) are significant at the 0.01 level and determine the adoption of drought-tolerant crop species. Access to information on climate change (0.536) is significant at the 0.01 level, indicating the adoption of improved crop species. Age (- 0.010), farm size (- 0.085) and crop insurance (0.206) were significant at the 0.05 level, while access to information on climate change (0.353) was significant at the 0.01 level and determined the adoption of off-farm job opportunities. The study recommends the availability and accessibility of credit, climate-smart agricultural practices, and the establishment of public‒private partnerships, among others.

Meta-Analysis of COVID-19 Metabolomics Identifies Variations in Robustness of Biomarkers.

The global COVID-19 pandemic resulted in widespread harms but also rapid advances in vaccine development, diagnostic testing, and treatment. As the disease moves to endemic status, the need to identify characteristic biomarkers of the disease for diagnostics or therapeutics has lessened, but lessons can still be learned to inform biomarker research in dealing with future pathogens. In this work, we test five sets of research-derived biomarkers against an independent targeted and quantitative Liquid Chromatography-Mass Spectrometry metabolomics dataset to evaluate how robustly these proposed panels would distinguish between COVID-19-positive and negative patients in a hospital setting. We further evaluate a crowdsourced panel comprising the COVID-19 metabolomics biomarkers most commonly mentioned in the literature between 2020 and 2023. The best-performing panel in the independent dataset-measured by F1 score (0.76) and AUROC (0.77)-included nine biomarkers: lactic acid, glutamate, aspartate, phenylalanine, ß-alanine, ornithine, arachidonic acid, choline, and hypoxanthine. Panels comprising fewer metabolites performed less well, showing weaker statistical significance in the independent cohort than originally reported in their respective discovery studies. Whilst the studies reviewed here were small and may be subject to confounders, it is desirable that biomarker panels be resilient across cohorts if they are to find use in the clinic, highlighting the importance of assessing the robustness and reproducibility of metabolomics analyses in independent populations.

Chromosomal alterations in sporadic medullary thyroid carcinoma and correlation with outcome.

Somatic copy number alterations (SCNA) involving either a whole chromosome or just one of the arms, or even smaller parts, have been described in about 88% of human tumors. This study investigated the SCNA profile in 40 well-characterized sporadic medullary thyroid carcinomas by comparative genomic hybridization array. We found that 26/40 (65%) cases had at least one SCNA. The prevalence of SCNA, and in particular of chromosome 3 and 10, was significantly higher in cases with a RET somatic mutation. Similarly, SCNA of chromosomes 3, 9, 10 and 16 were more frequent in cases with a worse outcome and an advanced disease. By the pathway enrichment analysis, we found a mutually exclusive distribution of biological pathways in metastatic, biochemically persistent and cured patients. In particular, we found gain of regions involved in the intracellular signaling and loss of regions involved in DNA repair and TP53 pathways in the group of metastatic patients. Gain of regions involved in the cell cycle and senescence were observed in patients with biochemical disease. Finally, gain of regions associated with the immune system and loss of regions involved in the apoptosis pathway were observed in cured patients suggesting a role of specific SCNA and corresponding altered pathways in the outcome of sporadic MTC.

An explainable model of host genetic interactions linked to COVID-19 severity.

We employed a multifaceted computational strategy to identify the genetic factors contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing (WES) dataset of a cohort of 2000 Italian patients. We coupled a stratified k-fold screening, to rank variants more associated with severity, with the training of multiple supervised classifiers, to predict severity based on screened features. Feature importance analysis from tree-based models allowed us to identify 16 variants with the highest support which, together with age and gender covariates, were found to be most predictive of COVID-19 severity. When tested on a follow-up cohort, our ensemble of models predicted severity with high accuracy (ACC = 81.88%; AUCROC = 96%; MCC = 61.55%). Our model recapitulated a vast literature of emerging molecular mechanisms and genetic factors linked to COVID-19 response and extends previous landmark Genome-Wide Association Studies (GWAS). It revealed a network of interplaying genetic signatures converging on established immune system and inflammatory processes linked to viral infection response. It also identified additional processes cross-talking with immune pathways, such as GPCR signaling, which might offer additional opportunities for therapeutic intervention and patient stratification. Publicly available PheWAS datasets revealed that several variants were significantly associated with phenotypic traits such as "Respiratory or thoracic disease", supporting their link with COVID-19 severity outcome.