Highly Species-Specific Foliar Metabolomes of Diverse Woody Species and Relationships with the Leaf Economics Spectrum

Plants show an extraordinary diversity in chemical composition and are characterized by different functional traits. However, relationships between the foliar primary and specialized metabolism in terms of metabolite numbers and composition as well as links with the leaf economics spectrum have rarely been explored. We investigated these relationships in leaves of 20 woody species from the Mediterranean region grown as saplings in a common garden, using a comparative ecometabolomics approach that included (semi-)polar primary and specialized metabolites. Our analyses revealed significant positive correlations between both the numbers and relative composition of primary and specialized metabolites. The leaf metabolomes were highly species-specific but in addition showed some phylogenetic imprints. Moreover, metabolomes of deciduous species were distinct from those of evergreens. Significant relationships were found between the primary metabolome and nitrogen content and carbon/nitrogen ratio, important traits of the leaf economics spectrum, ranging from acquisitive (mostly deciduous) to conservative (evergreen) leaves. A comprehensive understanding of various leaf traits and their coordination in different plant species may facilitate our understanding of plant functioning in ecosystems. Chemodiversity is thereby an important component of biodiversity

The Impact of Scientific Management Principles on Food Hub

Food hubs have seen substantial growth in the past few decades but the overall operational efficiency and effectiveness is a concern for the managers of these facilities. The experiment designed consisted of 60 participants divided into four treatment groups that simulated tasks completed at food hubs. The experiment designed was a 2x2 factorial design and each treatment group had five teams with three members. The treatment groups had all combinations of the two independent variables ‘training’ and ‘process improvement’ and the impact these factors made on time to pack (TP), time to stack (TS), number of errors while stacking (ES), and number of errors while packing (EP) were investigated. The results show that for TS, TP, and ES, both training and process improvement significantly increased the food hub’s efficiency. For EP, process improvement significantly reduced errors while training had less impact

A combined computational and functional approach identifies IGF2BP2 as a driver of chemoresistance in a wide array of pre-clinical models of colorectal cancer

Aim Chemoresistance is a major cause of treatment failure in colorectal cancer (CRC) therapy. In this study, the impact of the IGF2BP family of RNA-binding proteins on CRC chemoresistance was investigated using in silico, in vitro, and in vivo approaches. Methods Gene expression data from a well-characterized cohort and publicly available cross-linking immunoprecipi‑ tation sequencing (CLIP-Seq) data were collected. Resistance to chemotherapeutics was assessed in patient-derived xenografts (PDXs) and patient-derived organoids (PDOs). Functional studies were performed in 2D and 3D cell culture models, including proliferation, spheroid growth, and mitochondrial respiration analyses. Results We identifed IGF2BP2 as the most abundant IGF2BP in primary and metastastatic CRC, correlating with tumor stage in patient samples and tumor growth in PDXs. IGF2BP2 expression in primary tumor tissue was signif‑ cantly associated with resistance to selumetinib, geftinib, and regorafenib in PDOs and to 5-fuorouracil and oxalipl‑ atin in PDX in vivo. IGF2BP2 knockout (KO) HCT116 cells were more susceptible to regorafenib in 2D and to oxaliplatin, selumitinib, and nintedanib in 3D cell culture. Further, a bioinformatic analysis using CLIP data suggested stabiliza‑ tion of target transcripts in primary and metastatic tumors. Measurement of oxygen consumption rate (OCR) and extracellular acidifcation rate (ECAR) revealed a decreased basal OCR and an increase in glycolytic ATP production rate in IGF2BP2 KO. In addition, real-time reverse transcriptase polymerase chain reaction (qPCR) analysis confrmed decreased expression of genes of the respiratory chain complex I, complex IV, and the outer mitochondrial membrane in IGF2BP2 KO cells. Conclusions IGF2BP2 correlates with CRC tumor growth in vivo and promotes chemoresistance by altering mito‑ chondrial respiratory chain metabolism. As a druggable target, IGF2BP2 could be used in future CRC therapy to overcome CRC chemoresistance

Current Challenges in Plant Eco-Metabolomics

The relatively new research discipline of Eco-Metabolomics is the application of metabolomics techniques to ecology with the aim to characterise biochemical interactions of organisms across different spatial and temporal scales. Metabolomics is an untargeted biochemical approach to measure many thousands of metabolites in different species, including plants and animals. Changes in metabolite concentrations can provide mechanistic evidence for biochemical processes that are relevant at ecological scales. These include physiological, phenotypic and morphological responses of plants and communities to environmental changes and also interactions with other organisms. Traditionally, research in biochemistry and ecology comes from two different directions and is performed at distinct spatiotemporal scales. Biochemical studies most often focus on intrinsic processes in individuals at physiological and cellular scales. Generally, they take a bottom-up approach scaling up cellular processes from spatiotemporally fine to coarser scales. Ecological studies usually focus on extrinsic processes acting upon organisms at population and community scales and typically study top-down and bottom-up processes in combination. Eco-Metabolomics is a transdisciplinary research discipline that links biochemistry and ecology and connects the distinct spatiotemporal scales. In this review, we focus on approaches to study chemical and biochemical interactions of plants at various ecological levels, mainly plant–organismal interactions, and discuss related examples from other domains. We present recent developments and highlight advancements in Eco-Metabolomics over the last decade from various angles. We further address the five key challenges: (1) complex experimental designs and large variation of metabolite profiles; (2) feature extraction; (3) metabolite identification; (4) statistical analyses; and (5) bioinformatics software tools and workflows. The presented solutions to these challenges will advance connecting the distinct spatiotemporal scales and bridging biochemistry and ecology

Using next-generation sequencing for high resolution multiplex analysis of copy number variation from nanogram quantities of DNA from formalin-fixed paraffin-embedded specimens

The use of next-generation sequencing technologies to produce genomic copy number data has recently been described. Most approaches, however, reply on optimal starting DNA, and are therefore unsuitable for the analysis of formalin-fixed paraffin-embedded (FFPE) samples, which largely precludes the analysis of many tumour series. We have sought to challenge the limits of this technique with regards to quality and quantity of starting material and the depth of sequencing required. We confirm that the technique can be used to interrogate DNA from cell lines, fresh frozen material and FFPE samples to assess copy number variation. We show that as little as 5 ng of DNA is needed to generate a copy number karyogram, and follow this up with data from a series of FFPE biopsies and surgical samples. We have used various levels of sample multiplexing to demonstrate the adjustable resolution of the methodology, depending on the number of samples and available resources. We also demonstrate reproducibility by use of replicate samples and comparison with microarray-based comparative genomic hybridization (aCGH) and digital PCR. This technique can be valuable in both the analysis of routine diagnostic samples and in examining large repositories of fixed archival material

Prevalence and architecture of de novo mutations in developmental disorders.

The genomes of individuals with severe, undiagnosed developmental disorders are enriched in damaging de novo mutations (DNMs) in developmentally important genes. Here we have sequenced the exomes of 4,293 families containing individuals with developmental disorders, and meta-analysed these data with data from another 3,287 individuals with similar disorders. We show that the most important factors influencing the diagnostic yield of DNMs are the sex of the affected individual, the relatedness of their parents, whether close relatives are affected and the parental ages. We identified 94 genes enriched in damaging DNMs, including 14 that previously lacked compelling evidence of involvement in developmental disorders. We have also characterized the phenotypic diversity among these disorders. We estimate that 42% of our cohort carry pathogenic DNMs in coding sequences; approximately half of these DNMs disrupt gene function and the remainder result in altered protein function. We estimate that developmental disorders caused by DNMs have an average prevalence of 1 in 213 to 1 in 448 births, depending on parental age. Given current global demographics, this equates to almost 400,000 children born per year

Finding Diagnostically Useful Patterns in Quantitative Phenotypic Data.

Trio-based whole-exome sequence (WES) data have established confident genetic diagnoses in ∼40% of previously undiagnosed individuals recruited to the Deciphering Developmental Disorders (DDD) study. Here we aim to use the breadth of phenotypic information recorded in DDD to augment diagnosis and disease variant discovery in probands. Median Euclidean distances (mEuD) were employed as a simple measure of similarity of quantitative phenotypic data within sets of ≥10 individuals with plausibly causative de novo mutations (DNM) in 28 different developmental disorder genes. 13/28 (46.4%) showed significant similarity for growth or developmental milestone metrics, 10/28 (35.7%) showed similarity in HPO term usage, and 12/28 (43%) showed no phenotypic similarity. Pairwise comparisons of individuals with high-impact inherited variants to the 32 individuals with causative DNM in ANKRD11 using only growth z-scores highlighted 5 likely causative inherited variants and two unrecognized DNM resulting in an 18% diagnostic uplift for this gene. Using an independent approach, naive Bayes classification of growth and developmental data produced reasonably discriminative models for the 24 DNM genes with sufficiently complete data. An unsupervised naive Bayes classification of 6,993 probands with WES data and sufficient phenotypic information defined 23 in silico syndromes (ISSs) and was used to test a "phenotype first" approach to the discovery of causative genotypes using WES variants strictly filtered on allele frequency, mutation consequence, and evidence of constraint in humans. This highlighted heterozygous de novo nonsynonymous variants in SPTBN2 as causative in three DDD probands