A personalized stepwise dynamic predictive algorithm of the time to first treatment in chronic lymphocytic leukemia.

Personalized prediction is ideal in chronic lymphocytic leukemia (CLL). Although refined models have been developed, stratifying patients in risk groups, it is required to accommodate time-dependent information of patients, to address the clinical heterogeneity observed within these groups. In this direction, this study proposes a personalized stepwise dynamic predictive algorithm (PSDPA) for the time-to-first-treatment of the individual patient. The PSDPA introduces a personalized Score, reflecting the evolution in the patient's follow-up, employed to develop a reference pool of patients. Score evolution's similarity is used to predict, at a selected time point, the time-to-first-treatment for a new patient. Additional patient's biological information may be utilized. The algorithm was applied to 20 CLL patients, indicating that stricter assessment criteria for the Score evolution's similarity, and biological similarity exploitation, may improve prediction. The PSDPA capitalizes on both the follow-up and the biological background of the individual patient, dynamically promoting personalized prediction in CLL.

Single Nucleotide Polymorphisms' Causal Structure Robustness within Coronary Artery Disease Patients.

An ever-growing amount of accumulated data has materialized in several scientific fields, due to recent technological progress. New challenges emerge in exploiting these data and utilizing the valuable available information. Causal models are a powerful tool that can be employed towards this aim, by unveiling the structure of causal relationships between different variables. The causal structure may avail experts to better understand relationships, or even uncover new knowledge. Based on 963 patients with coronary artery disease, the robustness of the causal structure of single nucleotide polymorphisms was assessed, taking into account the value of the Syntax Score, an index that evaluates the complexity of the disease. The causal structure was investigated, both locally and globally, under different levels of intervention, reflected in the number of patients that were randomly excluded from the original datasets corresponding to two categories of the Syntax Score, zero and positive. It is shown that the causal structure of single nucleotide polymorphisms was more robust under milder interventions, whereas in the case of stronger interventions, the impact increased. The local causal structure around the Syntax Score was studied in the case of a positive Syntax Score, and it was found to be resilient, even when the intervention was strong. Consequently, employing causal models in this context may increase the understanding of the biological aspects of coronary artery disease.

A wide foodomics approach coupled with metagenomics elucidates the environmental signature of potatoes.

The term "terroir" has been widely employed to link differential geographic phenotypes with sensorial signatures of agricultural food products, influenced by agricultural practices, soil type, and climate. Nowadays, the geographical indications labeling has been developed to safeguard the quality of plant-derived food that is linked to a certain terroir and is generally considered as an indication of superior organoleptic properties. As the dynamics of agroecosystems are highly intricate, consisting of tangled networks of interactions between plants, microorganisms, and the surrounding environment, the recognition of the key molecular components of terroir fingerprinting remains a great challenge to protect both the origin and the safety of food commodities. Furthermore, the contribution of microbiome as a potential driver of the terroir signature has been underestimated. Herein, we present a first comprehensive view of the multi-omic landscape related to transcriptome, proteome, epigenome, and metagenome of the popular Protected Geographical Indication potatoes of Naxos.

Disclosing the molecular basis of salinity priming in olive trees using proteogenomic model discovery.

Plant responses to salinity are becoming increasingly understood, however, salt priming mechanisms remain unclear, especially in perennial fruit trees. Herein, we showed that low-salt pre-exposure primes olive (Olea europaea) plants against high salinity stress. We then performed a proteogenomic study to characterize priming responses in olive roots and leaves. Integration of transcriptomic and proteomic data along with metabolic data revealed robust salinity changes that exhibit distinct or overlapping patterns in olive tissues, among which we focused on sugar regulation. Using the multi-crossed -omics data set, we showed that major differences between primed and nonprimed tissues are mainly associated with hormone signaling and defense-related interactions. We identified multiple genes and proteins, including known and putative regulators, that reported significant proteomic and transcriptomic changes between primed and nonprimed plants. Evidence also supported the notion that protein post-translational modifications, notably phosphorylations, carbonylations and S-nitrosylations, promote salt priming. The proteome and transcriptome abundance atlas uncovered alterations between mRNA and protein quantities within tissues and salinity conditions. Proteogenomic-driven causal model discovery also unveiled key interaction networks involved in salt priming. Data generated in this study are important resources for understanding salt priming in olive tree and facilitating proteogenomic research in plant physiology.

Could Causal Discovery in Proteogenomics Assist in Understanding Gene-Protein Relations? A Perennial Fruit Tree Case Study Using Sweet Cherry as a Model.

Genome-wide transcriptome analysis is a method that produces important data on plant biology at a systemic level. The lack of understanding of the relationships between proteins and genes in plants necessitates a further thorough analysis at the proteogenomic level. Recently, our group generated a quantitative proteogenomic atlas of 15 sweet cherry (Prunus avium L.) cv. 'Tragana Edessis' tissues represented by 29,247 genes and 7584 proteins. The aim of the current study was to perform a targeted analysis at the gene/protein level to assess the structure of their relation, and the biological implications. Weighted correlation network analysis and causal modeling were employed to, respectively, cluster the gene/protein pairs, and reveal their cause-effect relations, aiming to assess the associated biological functions. To the best of our knowledge, this is the first time that causal modeling has been employed within the proteogenomics concept in plants. The analysis revealed the complex nature of causal relations among genes/proteins that are important for traits of interest in perennial fruit trees, particularly regarding the fruit softening and ripening process in sweet cherry. Causal discovery could be used to highlight persistent relations at the gene/protein level, stimulating biological interpretation and facilitating further study of the proteogenomic atlas in plants.

Whole-genome resequencing of Cucurbita pepo morphotypes to discover genomic variants associated with morphology and horticulturally valuable traits.

Cucurbita pepo contains two cultivated subspecies, each of which encompasses four fruit-shape morphotypes (cultivar groups). The Pumpkin, Vegetable Marrow, Cocozelle, and Zucchini Groups are of subsp. pepo and the Acorn, Crookneck, Scallop, and Straightneck Groups are of subsp. ovifera. Recently, a de novo assembly of the C. pepo subsp. pepo Zucchini genome was published, providing insights into its evolution. To expand our knowledge of evolutionary processes within C. pepo and to identify variants associated with particular morphotypes, we performed whole-genome resequencing of seven of these eight C. pepo morphotypes. We report for the first time whole-genome resequencing of the four subsp. pepo (Pumpkin, Vegetable Marrow, Cocozelle, green Zucchini, and yellow Zucchini) morphotypes and three of the subsp. ovifera (Acorn, Crookneck, and Scallop) morphotypes. A high-depth resequencing approach was followed, using the BGISEQ-500 platform that enables the identification of rare variants, with an average of 33.5X. Approximately 94.5% of the clean reads were mapped against the reference Zucchini genome. In total, 3,823,977 high confidence single-nucleotide polymorphisms (SNPs) were identified. Within each accession, SNPs varied from 636,918 in green Zucchini to 2,656,513 in Crookneck, and were distributed homogeneously along the chromosomes. Clear differences between subspecies pepo and ovifera in genetic variation and linkage disequilibrium are highlighted. In fact, comparison between subspecies pepo and ovifera indicated 5710 genes (22.5%) with Fst > 0.80 and 1059 genes (4.1%) with Fst = 1.00 as potential candidate genes that were fixed during the independent evolution and domestication of the two subspecies. Linkage disequilibrium was greater in subsp. ovifera than in subsp. pepo, perhaps reflective of the earlier differentiation of morphotypes within subsp. ovifera. Some morphotype-specific genes have been localized. Our results offer new clues that may provide an improved understanding of the underlying genomic regions involved in the independent evolution and domestication of the two subspecies. Comparisons among SNPs unique to particular subspecies or morphotypes may provide candidate genes responsible for traits of high economic importance.