Background: Xenacoelomorpha is a marine clade of microscopic worms that is an important model system for understanding the evolution of key bilaterian novelties, such as the excretory system. Nevertheless, Xenacoelomorpha genomics has been restricted to a few species that either can be cultured in the lab or are centimetres long. Thus far, no genomes are available for Nemertodermatida, one of the group's main clades and whose origin has been dated more than 400 million years ago.Methods: DNA was extracted from a single specimen and sequenced with HiFi following the PacBio Ultra-Low DNA Input protocol. After genome assembly, decontamination, and annotation, the genome quality was benchmarked using two acoel genomes and one Illumina genome as reference. The gene content of three cnidarians, three acoelomorphs, four deuterostomes, and eight protostomes was clustered in orthogroups to make inferences of gene content evolution. Finally, we focused on the genes related to the ultrafiltration excretory system to compare patterns of presence/absence and gene architecture among these clades.Results: We present the first nemertodermatid genome sequenced from a single specimen of Nemertoderma westbladi. Although genome contiguity remains challenging (N50: 60 kb), it is very complete (BUSCO: 80.2%, Metazoa; 88.6%, Eukaryota) and the quality of the annotation allows fine-detail analyses of genome evolution. Acoelomorph genomes seem to be relatively conserved in terms of the percentage of repeats, number of genes, number of exons per gene and intron size. In addition, a high fraction of genes present in both protostomes and deuterostomes are absent in Acoelomorpha. Interestingly, we show that all genes related to the excretory system are present in Xenacoelomorpha except Osr, a key element in the development of these organs and whose acquisition seems to be interconnected with the origin of the specialised excretory system.Conclusion: Overall, these analyses highlight the potential of the Ultra-Low Input DNA protocol and HiFi to generate high-quality genomes from single animals, even for relatively large genomes, making it a feasible option for sequencing challenging taxa, which will be an exciting resource for comparative genomics analyses.

Background: Xenacoelomorpha is a marine clade of microscopic worms that is an important model system for understanding the evolution of key bilaterian novelties, such as the excretory system. Nevertheless, Xenacoelomorpha genomics has been restricted to a few species that either can be cultured in the lab or are centimetres long. Thus far, no genomes are available for Nemertodermatida, one of the group’s main clades and whose origin has been dated more than 400 million years ago.

Methods: DNA was extracted from a single specimen and sequenced with HiFi following the PacBio Ultra-Low DNA Input protocol. After genome assembly, decontamination, and annotation, the genome quality was benchmarked using two acoel genomes and one Illumina genome as reference. The gene content of three cnidarians, three acoelomorphs, four deuterostomes, and eight protostomes was clustered in orthogroups to make inferences of gene content evolution. Finally, we focused on the genes related to the ultrafiltration excretory system to compare patterns of presence/absence and gene architecture among these clades.

Results: We present the first nemertodermatid genome sequenced from a single specimen of Nemertoderma westbladi. Although genome contiguity remains challenging (N50: 60 kb), it is very complete (BUSCO: 80.2%, Metazoa; 88.6%, Eukaryota) and the quality of the annotation allows fine-detail analyses of genome evolution. Acoelomorph genomes seem to be relatively conserved in terms of the percentage of repeats, number of genes, number of exons per gene and intron size. In addition, a high fraction of genes present in both protostomes and deuterostomes are absent in Acoelomorpha. Interestingly, we show that all genes related to the excretory system are present in Xenacoelomorpha except Osr, a key element in the development of these organs and whose acquisition seems to be interconnected with the origin of the specialised excretory system.

Conclusion: Overall, these analyses highlight the potential of the Ultra-Low Input DNA protocol and HiFi to generate high-quality genomes from single animals, even for relatively large genomes, making it a feasible option for sequencing challenging taxa, which will be an exciting resource for comparative genomics analyses.

Mapping of chromosomal regions harboring genetic polymorphisms that regulate complex traits is usually followed by a search for the causative mutations underlying the observed effects. This is often a challenging task even after fine mapping, as millions of base pairs including many genes will typically need to be investigated. Thus to trace the causative mutation(s) there is a great need for efficient bioinformatic strategies. Here, we searched for genes and mutations regulating growth in the Virginia chicken lines - an experimental population comprising two lines that have been divergently selected for body weight at 56 days for more than 50 generations. Several quantitative trait loci (QTL) have been mapped in an F2 intercross between the lines, and the regions have subsequently been replicated and fine mapped using an Advanced Intercross Line. We have further analyzed the QTL regions where the largest genetic divergence between the High-Weight selected (HWS) and Low-Weight selected (LWS) lines was observed. Such regions, covering about 37% of the actual QTL regions, were identified by comparing the allele frequencies of the HWS and LWS lines using both individual 60K SNP chip genotyping of birds and analysis of read proportions from genome resequencing of DNA pools. Based on a combination of criteria including significance of the QTL, allele frequency difference of identified mutations between the selected lines, gene information on relevance for growth, and the predicted functional effects of identified mutations we propose here a subset of candidate mutations of highest priority for further evaluation in functional studies. The candidate mutations were identified within the GCG, IGFBP2, GRB14, CRIM1, FGF16, VEGFR-2, ALG11, EDN1, SNX6, and BIRC7 genes. We believe that the proposed method of combining different types of genomic information increases the probability that the genes underlying the observed QTL effects are represented among the candidate mutations identified.

Species diversity is unequally distributed across the globe,with the greatest concentration occurring in the tropics. Even within the tropics, there are significant differences in the numbers of taxa found in each continental region. Manilkara is a pantropical genus of trees in the Sapotaceae comprising c.78 species. Its distribution allows for biogeographic investigation and testing of whether rates of diversification differ amongst tropical regions. The age and geographical origin of Manilkara are inferred to determine whether Gondwanan break-up, boreotropical migration or long distance dispersal have shaped its current disjunct distribution. Diversification rates through time are also analyzed to determine whether the timing and tempo of speciation on each continent coincides with geoclimatic events. Bayesian analyses of nuclear (ITS) and plastid (rpl32-trnL,rps16-trnK,and trnS-trnFM) sequences were used to reconstruct a species level phylogeny of Manilkara and related genera in the tribe Mimusopeae. Analyses of the nuclear data using a fossil-calibrated relaxed molecular clock indicate that Manilkara evolved 32–29 million years ago (Mya) in Africa. Lineages within the genus dispersed to the Neotropics 26–18 Mya and to Asia 28–15 Mya. Higher speciation rates are found in the Neotropical Manilkara clade than in either African or Asian clades. Dating of regional diversification correlates with known palaeoclimatic events. In South America, the divergence between Atlantic coastal forest and Amazonian clades coincides with the formation of drier Cerrado and Caatinga habitats between them. In Africa diversification coincides with Tertiary cycles of aridification an duplif tof the east African plateaux. In South east Asia dispersal may have been limited by the relatively recent emergence of land in New Guinea and islands further east c.10 Mya.

Apixaban is a direct oral anticoagulant, a factor Xa inhibitor, used for the prevention of ischemic stroke in patients with atrial fibrillation. Despite using recommended dosing a few patients might still experience bleeding or lack of efficacy that might be related to inappropriate drug exposure. We conducted a genome-wide association study using data from 1,325 participants in the pivotal phase three trial of apixaban with the aim to identify genetic factors affecting the pharmacokinetics of apixaban. A candidate gene analysis was also performed for pre-specified variants in ABCB1, ABCG2, CYP3A4, CYP3A5, and SULT1A1, with a subsequent analysis of all available polymorphisms within the candidate genes. Significant findings were further evaluated to assess a potential association with clinical outcome such as bleeding or thromboembolic events. No variant was consistently associated with an altered apixaban exposure on a genome-wide level. The candidate gene analyses showed a statistically significant association with a well-known variant in the drug transporter gene ABCG2 (c.421G > T, rs2231142). Patients carrying this variant had a higher exposure to apixaban [area under the curve (AUC), beta = 151 (95% CI 59-243), p = 0.001]. On average, heterozygotes displayed a 5% increase of AUC and homozygotes a 17% increase of AUC, compared with homozygotes for the wild-type allele. Bleeding or thromboembolic events were not significantly associated with ABCG2 rs2231142. This large genome-wide study demonstrates that genetic variation in the drug transporter gene ABCG2 is associated with the pharmacokinetics of apixaban. However, the influence of this finding on drug exposure was small, and further studies are needed to better understand whether it is of relevance for ischemic and bleeding events.

The small hive beetle (SHB), a social parasite of beehives, is native to sub-Saharan Africa and has spread to America, Europe, and Australia. Recently, these beetles invaded China, causing widespread colony collapses in the honeybee, Apis cerana. In this study, single nucleotide polymorphisms (SNPs) were identified in the beetle genome from its native range (Africa), a region that was invaded by SHBs nearly 30 years ago (America), and more recent invasions (Asia). The beetles in the United States formed the earliest branch and show signs of two decades of gene flow and local adaptation to differentiate this population from the native ones. The beetles in China were deep branched and showed the highest fixation index when compared to the US populations. The number of SNPs in overexpressed genes was significantly higher than the transcriptome. Gene-expression profiles presented here distinguish the characters between adult and larvae SHBs.

We present the case of a male patient who was ultimately diagnosed with Becker muscular dystrophy (BMD; MIM# 300376) after the onset of muscle weakness in his teens progressively led to significant walking difficulties in his twenties. A genetic diagnosis was pursued but initial investigation revealed no aberrations in the dystrophin gene (DMD), although immunohistochemistry and Western blot analysis suggested the diagnosis of dystrophinopathy. Eventually, after more than 10 years, an RNA analysis captured abnormal splicing where 154 nucleotides from intron 43 were inserted between exon 43 and 44 resulting in a frameshift and a premature stop codon. Normal splicing of the DMD gene was also observed. Additionally, a novel variant c.6291-13537A>G in DMD was confirmed in the genomic DNA of the patient. The predicted function of the variant aligns with the mRNA results. To conclude, we here demonstrate that mRNA analysis can guide the diagnosis of non-coding genetic variants in DMD.

Functional analysis of gene sets derived from experiments is typically done by pathway annotation. Although many algorithms exist for analyzing the association between a gene set and a pathway, an issue which is generally ignored is that gene sets often represent multiple pathways. In such cases an association to a pathway is weakened by the presence of genes associated with other pathways. A way to counteract this is to cluster the gene set into more homogenous parts before performing pathway analysis on each module. We explored whether network-based pre-clustering of a query gene set can improve pathway analysis. The methods MCL, Infomap, and MGclus were used to cluster the gene set projected onto the FunCoup network. We characterized how well these methods are able to detect individual pathways in multi-pathway gene sets, and applied each of the clustering methods in combination with four pathway analysis methods: Gene Enrichment Analysis, BinoX, NEAT, and ANUBIX. Using benchmarks constructed from the KEGG pathway database we found that clustering can be beneficial by increasing the sensitivity of pathway analysis methods and by providing deeper insights of biological mechanisms related to the phenotype under study. However, keeping a high specificity is a challenge. For ANUBIX, clustering caused a minor loss of specificity, while for BinoX and NEAT it caused an unacceptable loss of specificity. GEA had very low sensitivity both before and after clustering. The choice of clustering method only had a minor effect on the results. We show examples of this approach and conclude that clustering can improve overall pathway annotation performance, but should only be used if the used enrichment method has a low false positive rate.

Methylmercury (MeHg) is a developmental neurotoxicant, and one potential mechanism of MeHg toxicity is epigenetic dysregulation. In a recent meta-analysis of epigenome-wide association studies (EWAS), associations between prenatal MeHg exposure and DNA methylation at several genomic sites were identified in blood from newborns and children. While EWASs reveal human-relevant associations, experimental studies are required to validate the relationship between exposure and DNA methylation changes, and to assess if such changes have implications for gene expression. Herein, we studied DNA methylation and gene expression of five of the top genes identified in the EWAS meta-analysis, MED31, MRPL19, GGH, GRK1, and LYSMD3, upon MeHg exposure in human SH-SY5Y cells exposed to 8 or 40 nM of MeHg during differentiation, using bisulfite-pyrosequencing and qPCR, respectively. The concentrations were selected to cover the range of MeHg concentrations in cord blood (2-8.5 mu g/L) observed in the cohorts included in the EWAS. Exposure to MeHg increased DNA methylation at MED31, a transcriptional regulator essential for fetal development. The results were in concordance with the epidemiological findings where more MED31 methylation was associated with higher concentrations of MeHg. Additionally, we found a non-significant decrease in DNA methylation at GGH, which corresponds to the direction of change observed in the EWAS, and a significant correlation of GGH methylation with its expression. In conclusion, this study corroborates some of the EWAS findings and puts forward candidate genes involved in MeHg's effects on the developing brain, thus highlighting the value of experimental validation of epidemiological association studies.

Methylmercury (MeHg) is a developmental neurotoxicant, and one potential mechanism of MeHg toxicity is epigenetic dysregulation. In a recent meta-analysis of epigenome-wide association studies (EWAS), associations between prenatal MeHg exposure and DNA methylation at several genomic sites were identified in blood from newborns and children. While EWASs reveal human-relevant associations, experimental studies are required to validate the relationship between exposure and DNA methylation changes, and to assess if such changes have implications for gene expression. Herein, we studied DNA methylation and gene expression of five of the top genes identified in the EWAS meta-analysis, MED31, MRPL19, GGH, GRK1, and LYSMD3, upon MeHg exposure in human SH-SY5Y cells exposed to 8 or 40 nM of MeHg during differentiation, using bisulfite-pyrosequencing and qPCR, respectively. The concentrations were selected to cover the range of MeHg concentrations in cord blood (2–8.5 μg/L) observed in the cohorts included in the EWAS. Exposure to MeHg increased DNA methylation at MED31, a transcriptional regulator essential for fetal development. The results were in concordance with the epidemiological findings where more MED31 methylation was associated with higher concentrations of MeHg. Additionally, we found a non-significant decrease in DNA methylation at GGH, which corresponds to the direction of change observed in the EWAS, and a significant correlation of GGH methylation with its expression. In conclusion, this study corroborates some of the EWAS findings and puts forward candidate genes involved in MeHg’s effects on the developing brain, thus highlighting the value of experimental validation of epidemiological association studies.

Background and Objective: Agranulocytosis is a rare and potentially life-threatening complication of metamizole (dipyrone) intake that is characterized by a loss of circulating neutrophil granulocytes. While the mechanism underlying this adverse drug reaction is not well understood, involvement of the immune system has been suggested. In addition, associations between genetic variants in the Human Leukocyte Antigen (HLA) region and agranulocytosis induced by other drugs have been reported. The aim of the present study was to assess whether genetic variants in classical HLA genes are associated with the susceptibility to metamizole-induced agranulocytosis (MIA) in a European population by targeted resequencing of eight HLA genes.

Design: A case-control cohort of Swiss patients with a history of neutropenia or agranulocytosis associated with metamizole exposure (n = 53), metamizole-tolerant (n = 39) and unexposed controls (n = 161) was recruited for this study. A high-throughput resequencing (HTS) and high-resolution typing method was used to sequence and analyze eight HLA loci in a discovery subset of this cohort (n = 31 cases, n = 38 controls). Identified candidate alleles were investigated in the full Swiss cohort as well as in two independent cohorts from Germany and Spain using HLA imputation from genome-wide SNP array data. In addition, variant calling based on HTS data was performed in the discovery subset for the class I genes HLA-A, -B, and -C using the HLA-specific mapper hla-mapper.

Results: Eight candidate alleles (p < 0.05) were identified in the discovery subset, of which HLA-C^∗04:01 was associated with MIA in the full Swiss cohort (p < 0.01) restricted to agranulocytosis (ANC < 0.5 × 10⁹/L) cases. However, no candidate allele showed a consistent association in the Swiss, German and Spanish cohorts. Analysis of individual sequence variants in class I genes produced consistent results with HLA typing but did not reveal additional small nucleotide variants associated with MIA.

Conclusion: Our results do not support an HLA-restricted T cell-mediated immune mechanism for MIA. However, we established an efficient high-resolution (three-field) eight-locus HTS HLA resequencing method to interrogate the HLA region and demonstrated the feasibility of its application to pharmacogenetic studies.

Introduction: Educational attainment, widely used in epidemiologic studies as a surrogate for socioeconomic status, is a predictor of cardiovascular health outcomes.

Methods: A two-stage genome-wide meta-analysis of low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), and triglyceride (TG) levels was performed while accounting for gene-educational attainment interactions in up to 226,315 individuals from five population groups. We considered two educational attainment variables: “Some College” (yes/no, for any education beyond high school) and “Graduated College” (yes/no, for completing a 4-year college degree). Genome-wide significant (p < 5 × 10−8) and suggestive (p < 1 × 10−6) variants were identified in Stage 1 (in up to 108,784 individuals) through genome-wide analysis, and those variants were followed up in Stage 2 studies (in up to 117,531 individuals).

Results: In combined analysis of Stages 1 and 2, we identified 18 novel lipid loci (nine for LDL, seven for HDL, and two for TG) by two degree-of-freedom (2 DF) joint tests of main and interaction effects. Four loci showed significant interaction with educational attainment. Two loci were significant only in cross-population analyses. Several loci include genes with known or suggested roles in adipose (FOXP1, MBOAT4, SKP2, STIM1, STX4), brain (BRI3, FILIP1, FOXP1, LINC00290, LMTK2, MBOAT4, MYO6, SENP6, SRGAP3, STIM1, TMEM167A, TMEM30A), and liver (BRI3, FOXP1) biology, highlighting the potential importance of brain-adipose-liver communication in the regulation of lipid metabolism. An investigation of the potential druggability of genes in identified loci resulted in five gene targets shown to interact with drugs approved by the Food and Drug Administration, including genes with roles in adipose and brain tissue.

Discussion: Genome-wide interaction analysis of educational attainment identified novel lipid loci not previously detected by analyses limited to main genetic effects.

Endothelial cell (EC) plays critical roles in vascular physiological and pathological processes. With the development of high-throughput technologies, transcriptomics analysis of EC has increased dramatically and a large amount of informative data have been generated. The dynamic patterns of gene expression in ECs under various conditions were revealed. Unfortunately, due to the lack of bioinformatics infrastructures, reuse of these large-scale datasets is challenging for many scientists. Here, by systematic re-analyzing, integrating, and standardizing of 203 RNA sequencing samples from freshly isolated mouse ECs under 71 conditions, we constructed an integrated mouse EC gene expression omnibus (ECO). The ECO database enables one-click retrieval of endothelial expression profiles from different organs under different conditions including disease models, genetic modifications, and clinically relevant treatments in vivo. The EC expression profiles are visualized with user-friendly bar-plots. It also provides a convenient search tool for co-expressed genes. ECO facilitates endothelial research with an integrated tool and resource for transcriptome analysis. The ECO database is freely available at https://heomics. shinyapps.io/ecodb/.

Most biological traits are regulated by a complex interplay between genetic and environmental factors. By intercrossing divergent lines, it is possible to identify individual and interacting QTL involved in the genetic architecture of these traits. When the loci have been mapped, alternative strategies are needed for fine-mapping and studying the individual and interactive effects of the QTL in detail. We have previously identified, replicated, and fine mapped a four-locus QTL network that determines nearly half of the eightfold difference in body weight at 56 days of age between two divergently selected chicken lines. Here, we describe, to our knowledge, the first generation of a three-locus QTL introgression line in chickens. Recurrent marker-assisted backcrossing was used to simultaneously transfer QTL alleles from the low-weight selected line into the high-weight selected line. Three generations of backcrossing and one generation of intercrossing resulted in an introgression line where all three introgressed QTL and several unlinked and linked control-loci were segregating at nearly expected allele frequencies. We show how intensive selection can be applied using artificial insemination to rapidly generate a multi-locus introgression line and provide recommendations for future breeding of introgression lines. This confirmed introgression line will facilitate later detailed studies of the effects of genetic interactions on complex traits in this population, including growth, and body-composition traits.

Understanding the evolutionary emergence and subsequent diversification of the vertebrate skeleton requires a comprehensive view of the diverse skeletal cell types found in distinct developmental contexts, tissues, and species. To date, our knowledge of the molecular nature of the shark calcified extracellular matrix, and its relationships with osteichthyan skeletal tissues, remain scarce. Here, based on specific combinations of expression patterns of the Col1a1, Col1a2, and Col2a1 fibrillar collagen genes, we compare the molecular footprint of endoskeletal elements from the chondrichthyan Scyliorhinus canicula and the tetrapod Xenopus tropicalis. We find that, depending on the anatomical location, Scyliorhinus skeletal calcification is associated to cell types expressing different subsets of fibrillar collagen genes, such as high levels of Col1a1 and Col1a2 in the neural arches, high levels of Col2a1 in the tesserae, or associated to a drastic Col2a1 downregulation in the centrum. We detect low Col2a1 levels in Xenopus osteoblasts, thereby revealing that the osteoblastic expression of this gene was significantly reduced in the tetrapod lineage. Finally, we uncover a striking parallel, from a molecular and histological perspective, between the vertebral cartilage calcification of both species and discuss the evolutionary origin of endochondral ossification.

Autism spectrum disorder (ASD) is a heterogeneous neuropsychiatric disorder with a complex genetic background. Analysis of altered molecular processes in ASD patients requires linear and nonlinear methods that provide interpretable solutions. Interpretable machine learning provides legible models that allow explaining biological mechanisms and support analysis of clinical subgroups. In this work, we investigated several case-control studies of gene expression measurements of ASD individuals. We constructed a rule-based learning model from three independent datasets that we further visualized as a nonlinear gene-gene co-predictive network. To find dissimilarities between ASD subtypes, we scrutinized a topological structure of the network and estimated a centrality distance. Our analysis revealed that autism is the most severe subtype of ASD, while pervasive developmental disorder-not otherwise specified and Asperger syndrome are closely related and milder ASD subtypes. Furthermore, we analyzed the most important ASD-related features that were described in terms of gene co-predictors. Among others, we found a strong co-predictive mechanism between EMC4 and TMEM30A, which may suggest a co-regulation between these genes. The present study demonstrates the potential of applying interpretable machine learning in bioinformatics analyses. Although the proposed methodology was designed for transcriptomics data, it can be applied to other omics disciplines.

We present the case of a male patient who was ultimately diagnosed with Becker muscular dystrophy (BMD; MIM# 300376) after the onset of muscle weakness in his teens progressively led to significant walking difficulties in his twenties. A genetic diagnosis was pursued but initial investigation revealed no aberrations in the dystrophin gene (DMD), although immunohistochemistry and Western blot analysis suggested the diagnosis of dystrophinopathy. Eventually, after more than 10 years, an RNA analysis captured abnormal splicing where 154 nucleotides from intron 43 were inserted between exon 43 and 44 resulting in a frameshift and a premature stop codon. Normal splicing of the DMD gene was also observed. Additionally, a novel variant c.6291–13537A>G in DMD was confirmed in the genomic DNA of the patient. The predicted function of the variant aligns with the mRNA results. To conclude, we here demonstrate that mRNA analysis can guide the diagnosis of non-coding genetic variants in DMD.

Aim: This study aimed to accurately identification of potential miRNAs for gastric cancer (GC) diagnosis at the early stages of the disease.

Methods: We used GSE106817 data with 2,566 miRNAs to train the machine learning models. We used the Boruta machine learning variable selection approach to identify the strong miRNAs associated with GC in the training sample. We then validated the prediction models in the independent sample GSE113486 data. Finally, an ontological analysis was done on identified miRNAs to eliciting the relevant relationships.

Results: Of those 2,874 patients in the training the model, there were 115 (4%) patients with GC. Boruta identified 30 miRNAs as potential biomarkers for GC diagnosis and hsa-miR-1343-3p was at the highest ranking. All of the machine learning algorithms showed that using hsa-miR-1343-3p as a biomarker, GC can be predicted with very high precision (AUC; 100%, sensitivity; 100%, specificity; 100% ROC; 100%, Kappa; 100) using with the cut-off point of 8.2 for hsa-miR-1343-3p. Also, ontological analysis of 30 identified miRNAs approved their strong relationship with cancer associated genes and molecular events.

Conclusion: The hsa-miR-1343-3p could be introduced as a valuable target for studies on the GC diagnosis using reliable biomarkers.

Background: High nerve density in tumors and metastasis via nerves (perineural invasion—PNI) have been reported extensively in solid tumors throughout the body including pancreatic, head and neck, gastric, prostate, breast, and colorectal cancers. Ablation of tumor nerves results in improved disease outcomes, suggesting that blocking nerve–tumor communication could be a novel treatment strategy. However, the molecular mechanisms underlying this remain poorly understood. Thus, the aim here was to identify molecular pathways underlying nerve–tumor crosstalk and to determine common molecular features between PNI-associated cancers.

Results: Analysis of head and neck (HNSCC), pancreatic, and gastric (STAD) cancer Gene Expression Omnibus datasets was used to identify differentially expressed genes (DEGs). This revealed extracellular matrix components as highly dysregulated. To enrich for pathways associated with PNI, genes previously correlated with PNI in STAD and in 2 HNSCC studies where tumor samples were segregated by PNI status were analyzed. Neurodevelopmental genes were found to be enriched with PNI. In datasets where tumor samples were not segregated by PNI, neurodevelopmental pathways accounted for 12%–16% of the DEGs. Further dysregulation of axon guidance genes was common to all cancers analyzed. By examining paralog genes, a clear pattern emerged where at least one family member from several axon guidance pathways was affected in all cancers examined. Overall 17 different axon guidance gene families were disrupted, including the ephrin–Eph, semaphorin–neuropilin/plexin, and slit–robo pathways. These findings were validated using The Cancer Genome Atlas and cross-referenced to other cancers with a high incidence of PNI including colon, cholangiocarcinoma, prostate, and breast cancers. Survival analysis revealed that the expression levels of neurodevelopmental gene families impacted disease survival.

Conclusion: These data highlight the importance of the tumor as a source of signals for neural tropism and neural plasticity as a common feature of cancer. The analysis supports the hypothesis that dysregulation of neurodevelopmental programs is a common feature associated with PNI. Furthermore, the data suggested that different cancers may have evolved to employ alternative genetic strategies to disrupt the same pathways. Overall, these findings provide potential druggable targets for novel therapies of cancer management and provide multi-cancer molecular biomarkers.

The need for systematic drug repurposing has seen a steady increase over the past decade and may be particularly valuable to quickly remedy unexpected pandemics. The abundance of functional interaction data has allowed mapping of substantial parts of the human interactome modeled using functional association networks, favoring network-based drug repurposing. Network crosstalk-based approaches have never been tested for drug repurposing despite their success in the related and more mature field of pathway enrichment analysis. We have, therefore, evaluated the top performing crosstalk-based approaches for drug repurposing. Additionally, the volume of new interaction data as well as more sophisticated network integration approaches compelled us to construct a new benchmark for performance assessment of network-based drug repurposing tools, which we used to compare network crosstalk-based methods with a state-of-the-art technique. We find that network crosstalk-based drug repurposing is able to rival the state-of-the-art method and in some cases outperform it.

Ovarian cancer is the second most dangerous gynecologic cancer with a high mortality rate. The classification of gene expression data from high-dimensional and small-sample gene expression data is a challenging task. The discovery of miRNAs, a small non-coding RNA with 18-25 nucleotides in length that regulates gene expression, has revealed the existence of a new array for regulation of genes and has been reported as playing a serious role in cancer. By using LASSO and Elastic Net as embedded algorithms of feature selection techniques, the present study identified 10 miRNAs that were regulated in ovarian serum cancer samples compared to non-cancer samples in public available dataset GSE106817: hsa-miR-5100, hsa-miR-6800-5p, hsa-miR-1233-5p, hsa-miR-4532, hsa-miR-4783-3p, hsa-miR-4787-3p, hsa-miR-1228-5p, hsa-miR-1290, hsa-miR-3184-5p, and hsa-miR-320b. Further, we implemented state-of-the-art machine learning classifiers, such as logistic regression, random forest, artificial neural network, XGBoost, and decision trees to build clinical prediction models. Next, the diagnostic performance of these models with identified miRNAs was evaluated in the internal (GSE106817) and external validation dataset (GSE113486) by ROC analysis. The results showed that first four prediction models consistently yielded an AUC of 100%. Our findings provide significant evidence that the serum miRNA profile represents a promising diagnostic biomarker for ovarian cancer.

Evidence from observational studies suggests that increased exposure to calcium may increase the risk of coronary heart disease and stroke whereas magnesium might have a protective effect on disease risk. However, studies of the associations of these minerals with heart failure are scarce and limited by potential biases introduced by confounding and reverse causality. We applied a two-sample Mendelian randomization design using summary estimates to assess whether serum calcium and magnesium concentrations are causally associated with heart failure. Summary statistics data were collected for seven and six single-nucleotide polymorphisms associated with calcium and magnesium, respectively, from the hitherto largest genome-wide association studies on these minerals. Corresponding summary statistics for genetic associations with heart failure were available from publicly available data based on the UK Biobank study and based on participants of European ancestry. The findings showed that neither serum calcium nor magnesium concentrations were associated with heart failure. In the standard inverse-variance weighted analysis, the odds ratios of heart failure per genetically predicted one standard deviation increase in mineral concentrations were 0.89 (95% confidence interval 0.67-1.17; p = 0.41) for serum calcium and 0.89 (95% confidence interval 0.72-1.10; p = 0.28) for serum magnesium. Results were robust in sensitivity analyses, including the weighted median and Mendelian randomization Egger analyses. In conclusion, these findings do not support previous findings suggesting a link between serum calcium and magnesium and heart failure, but this study was underpowered to detect weak associations.

Exposure to Per- and polyfluoroalkyl substances (PFAS) has been linked to multiple undesirable health outcomes across a full lifespan, both in animal models as well as in human epidemiological studies. Immunosuppressive effects of PFAS have been reported, including increased risk of infections and suppressed vaccination responses in early childhood, as well as association with immunotoxicity and diabetes. On a mechanistic level, PFAS exposure has been linked with metabolic disturbances, particularly in lipid metabolism, but the underlying mechanisms are poorly characterized. Herein we explore lipidomic signatures of prenatal and early-life exposure to perfluoroundecanoic acid (PFUnDA) in non-obese diabetic (NOD) mice; an experimental model of autoimmune diabetes. Female NOD mice were exposed to four levels of PFUnDA in drinking water at mating, during gestation and lactation, and during the first weeks of life of female offspring. At offspring age of 11-12 weeks, insulitis and immunological endpoints were assessed, and serum samples were collected for comprehensive lipidomic analyses. We investigated the associations between exposure, lipidomic profile, insulitis grade, number of macrophages and apoptotic, active-caspase-3-positive cells in pancreatic islets. Dose-dependent changes in lipidomic profiles in mice exposed to PFUnDA were observed, with most profound changes seen at the highest exposure levels. Overall, PFUnDA exposure caused downregulation of phospholipids and triacylglycerols containing polyunsaturated fatty acids. Our results show that PFUnDA exposure in NOD mice alters lipid metabolism and is associated with pancreatic insulitis grade. Moreover, the results are in line with those reported in human studies, thus suggesting NOD mice as a suitable model to study the impacts of environmental chemicals on T1D.

Blood levels of growth differentiation factor-15 (GDF-15), also known as macrophage inhibitory cytokine-1 (MIC-1), have been associated with various pathological processes and diseases, including cardiovascular disease and cancer. Prior studies suggest genetic factors play a role in regulating blood MIC-1/GDF-15 concentration. In the current study, we conducted the largest genome-wide association study (GWAS) to date using a sample of similar to 5,400 community-based Caucasian participants, to determine the genetic variants associated with MIC-1/GDF-15 blood concentration. Conditional and joint (COJO), gene-based association, and gene-set enrichment analyses were also carried out to identify novel loci, genes, and pathways. Consistent with prior results, a locus on chromosome 19, which includes nine single nucleotide polymorphisms (SNPs) (top SNP, rs888663, p = 1.690 x 10(-35)), was significantly associated with blood MIC-1/GDF-15 concentration, and explained 21.47% of its variance. COJO analysis showed evidence for two independent signals within this locus. Gene-based analysis confirmed the chromosome 19 locus association and in addition, a putative locus on chromosome 1. Gene-set enrichment analyses showed that the "COPI-mediated anterograde transport" gene-set was associated with MIC-1/GDF15 blood concentration with marginal significance after FDR correction (p = 0.067). In conclusion, a locus on chromosome 19 was associated with MIC-1/GDF-15 blood concentration with genome-wide significance, with evidence for a new locus (chromosome 1). Future studies using independent cohorts are needed to confirm the observed associations especially for the chromosomes 1 locus, and to further investigate and identify the causal SNPs that contribute to MIC-1/GDF-15 levels.

Global microarray gene expression analyses previously demonstrated differences in female and male embryos during neurodevelopment. In particular, before sexual maturation of the gonads, the differences seem to concentrate on the expression of genes encoded on the X- and Y-chromosomes. To investigate genome-wide differences in expression during this early developmental window, we combined high-resolution RNA sequencing with qPCR to analyze brain samples from human embryos during the first trimester of development. Our analysis was tailored for maximum sensitivity to discover Y-chromosome gene expression, but at the same time, it was underpowered to detect X-inactivation escapees. Using this approach, we found that 5 out of 13 expressed gametolog pairs showed unbalanced gene dosage, and as a consequence, a male-biased expression. In addition, we found six novel non-annotated long non-coding RNAs on the Y-chromosome with conserved expression patterns in newborn chimpanzee. The tissue specific and time-restricted expression of these long non-coding RNAs strongly suggests important functions during central nervous system development in human males.

Polyploidy plays a major role in plant evolution. The establishment of new polyploids is often a consequence of a single or few successful polyploidization events occurring within a species' evolutionary trajectory. New polyploid lineages can play different roles in plant diversification and go through several evolutionary stages influenced by biotic and abiotic constraints and characterized by extensive genetic changes. The study of such changes has been crucial for understanding polyploid evolution. Here, we use the multiploid-species Paspalum intermedium to study population-level genetic and morphological variation and ecological differentiation in polyploids. Using flow cytometry, amplified fragment length polymorphism (AFLP) genetic markers, environmental variables, and morphological data, we assessed variations in ploidy, reproductive modes, and the genetic composition in 35 natural populations of P. intermedium along a latitudinal gradient in South America. Our analyses show that apomictic auto-tetraploids are of multiple independent origin. While overall genetic variation was higher in diploids, both diploids and tetraploids showed significant variation within and among populations. The spatial distribution of genetic variation provides evidence for a primary origin of the contact zone between diploids and tetraploids and further supports the hypothesis of geographic displacement between cytotypes. In addition, a strong link between the ecological differentiation of cytotypes and spatial distribution of genetic variation was observed. Overall, the results indicate that polyploidization in P. intermedium is a recurrent phenomenon associated to a shift in reproductive mode and that multiple polyploid lineages from genetically divergent diploids contributed to the successful establishment of local polyploid populations and dispersal into new environments.</p>

The etiology of hereditary ataxia syndromes is heterogeneous, and the mechanisms underlying these disorders are often unknown. Here, we utilized exome sequencing in two siblings with progressive ataxia and muscular weakness and identified a novel homozygous splice mutation (c.3020-1G > A) in neurofascin (NFASC). In RNA extracted from fibroblasts, we showed that the mutation resulted in inframe skipping of exon 26, with a deprived expression of the full-length transcript that corresponds to NFASC isoform NF186. To further investigate the disease mechanisms, we reprogrammed fibroblasts from one affected sibling to induced pluripotent stem cells, directed them to neuroepithelial stem cells and finally differentiated to neurons. In early neurogenesis, differentiating cells with selective depletion of the NF186 isoform showed significantly reduced neurite outgrowth as well as fewer emerging neurites. Furthermore, whole-cell patch-clamp recordings of patient-derived neuronal cells revealed a lower threshold for openings, indicating altered Na+ channel kinetics, suggesting a lower threshold for openings as compared to neuronal cells without the NFASC mutation. Taken together, our results suggest that loss of the full-length NFASC isoform NF186 causes perturbed neurogenesis and impaired neuronal biophysical properties resulting in a novel early-onset autosomal recessive ataxia syndrome.

Cancer subtype identification is important to facilitate cancer diagnosis and select effective treatments. Clustering of cancer patients based on high-dimensional RNA-sequencing data can be used to detect novel subtypes, but only a subset of the features (e.g., genes) contains information related to the cancer subtype. Therefore, it is reasonable to assume that the clustering should be based on a set of carefully selected features rather than all features. Several feature selection methods have been proposed, but how and when to use these methods are still poorly understood. Thirteen feature selection methods were evaluated on four human cancer data sets, all with known subtypes (gold standards), which were only used for evaluation. The methods were characterized by considering mean expression and standard deviation (SD) of the selected genes, the overlap with other methods and their clustering performance, obtained comparing the clustering result with the gold standard using the adjusted Rand index (ARI). The results were compared to a supervised approach as a positive control and two negative controls in which either a random selection of genes or all genes were included. For all data sets, the best feature selection approach outperformed the negative control and for two data sets the gain was substantial with ARI increasing from (−0.01, 0.39) to (0.66, 0.72), respectively. No feature selection method completely outperformed the others but using the dip-rest statistic to select 1000 genes was overall a good choice. The commonly used approach, where genes with the highest SDs are selected, did not perform well in our study.

Glioblastoma (GBM) is an aggressive type of brain cancer with a poor prognosis for affected patients. The current line of treatment only gives the patients a survival time of on average 15 months. In this work, we use genome-scale metabolic models (GEMs) together with other systems biology tools to examine the global transcriptomics-data of GBM-patients obtained from The Cancer Genome Atlas (TCGA). We reveal the molecular mechanisms underlying GBM and identify potential therapeutic targets for effective treatment of patients. The work presented consists of two main parts. The first part stratifies the patients into two groups, high and low survival, and compares their gene expression. The second part uses GBM and healthy brain tissue GEMs to simulate gene knockout in a GBM cell model to find potential therapeutic targets and predict their side effect in healthy brain tissue. We (1) find that genes upregulated in the patients with low survival are linked to various stages of the glioma invasion process, and (2) identify five essential genes for GBM, whose inhibition is non-toxic to healthy brain tissue, therefore promising to investigate further as therapeutic targets.

Glioblastoma (GBM) is an aggressive type of brain cancer with a poor prognosis for affected patients. The current line of treatment only gives the patients a survival time of on average 15 months. In this work, we use genome-scale metabolic models (GEMs) together with other systems biology tools to examine the global transcriptomics-data of GBM-patients obtained from The Cancer Genome Atlas (TCGA). We reveal the molecular mechanisms underlying GBM and identify potential therapeutic targets for effective treatment of patients. The work presented consists of two main parts. The first part stratifies the patients into two groups, high and low survival, and compares their gene expression. The second part uses GBM and healthy brain tissue GEMs to simulate gene knockout in a GBM cell model to find potential therapeutic targets and predict their side effect in healthy brain tissue. We (1) find that genes upregulated in the patients with low survival are linked to various stages of the glioma invasion process, and (2) identify five essential genes for GBM, whose inhibition is non-toxic to healthy brain tissue, therefore promising to investigate further as therapeutic targets.

Matrix Gla protein (Mgp) and bone Gla protein (Bgp) are vitamin-K dependent proteins that bind calcium in their gamma-carboxylated versions in mammals. They are recognized as positive (Bgp) or negative (Mgp and Bgp) regulators of biomineralization in a number of tissues, including skeletal tissues of bony vertebrates. The Mgp/Bgp gene family is poorly known in cartilaginous fishes, which precludes the understanding of the evolution of the biomineralization toolkit at the emergence of jawed vertebrates. Here we took advantage of recently released genomic and transcriptomic data in cartilaginous fishes and described the genomic loci and gene expression patterns of the Mgp/Bgp gene family. We identified three genes, Mgp1, Mgp2, and Bgp, in cartilaginous fishes instead of the single previously reported Mgp gene. We describe their genomic loci, resulting in a dynamic evolutionary scenario for this gene family including several events of local (tandem) duplications, but also of translocation events, along jawed vertebrate evolution. We describe the expression patterns of Mgp1, Mgp2, and Bgp in embryonic stages covering organogenesis in the small-spotted catshark Scyliorhinus canicula and present a comparative analysis with Mgp/Bgp family members previously described in bony vertebrates, highlighting ancestral features such as early embryonic, soft tissues, and neuronal expressions, but also derived features of cartilaginous fishes such as expression in fin supporting fibers. Our results support an ancestral function of Mgp in skeletal mineralization and a later derived function of Bgp in skeletal development that may be related to the divergence of bony vertebrates.

Chickens are bred all over the world and have significant economic value as one of the major agricultural animals. The growth rate of commercial broiler chickens is several times higher than its Red Jungle fowl (RJF) ancestor. To further improve the meat production of commercial chickens, it is quite important to decipher the genetic mechanism of chicken growth traits. In this study, we found that broiler chickens exhibited lower levels of E3 ubiquitin ligase muscle atrophy F-box (MAFbx or Atrogin-1) relative to its RJF ancestor. As a ubiquitin ligase, Atrogin-1 plays a crucial role in muscle development in which its up-regulation often indicates the activation of muscle atrophic pathways. Here, we showed that the Atrogin-1 expression variance partly affects chicken muscle growth rates among different breeds. Furthermore, we demonstrated that the reduced expression of Atrogin-1 in broiler chickens was ascribed to a single nucleotide polymorphism (SNP), which inhibited the binding of transcription regulators and attenuated the enhancer activity. The decreased Atrogin-1 in broiler chickens suppresses the catabolism of muscle protein and preserves muscle mass. Our study facilitates the understanding of the molecular mechanism of chicken muscle development and has a high translational impact in chicken breeding.

BACKGROUND: Non-synonymous single-nucleotide polymorphisms (nsSNPs) within the coding regions of genes causing amino acid substitutions (AASs) may have a large impact on protein function. The possibilities to identify nsSNPs across genomes have increased notably with the advent of next-generation sequencing technologies. Thus, there is a strong need for efficient bioinformatics tools to predict the functional effect of AASs. Such tools can be used to identify the most promising candidate mutations for further experimental validation.

RESULTS: Here we present prediction of AAS effects (PASE), a novel method that predicts the effect of an AASs based on physicochemical property changes. Evaluation of PASE, using a few AASs of known phenotypic effects and 3338 human AASs, for which functional effects have previously been scored with the widely used SIFT and PolyPhen tools, show that PASE is a useful method for functional prediction of AASs. We also show that the predictions can be further improved by combining PASE with information about evolutionary conservation.

CONCLUSION: PASE is a novel algorithm for predicting functional effects of AASs, which can be used for pinpointing the most interesting candidate mutations. PASE predictions are based on changes in seven physicochemical properties and can improve predictions from many other available tools, which are based on evolutionary conservation. Using available experimental data and predictions from the already existing tools, we demonstrate that PASE is a useful method for predicting functional effects of AASs, even when a limited number of query sequence homologs/orthologs are available.

Among the three genomes in plant cells, the mitochondrial genome (mitogenome) is the least studied due to complex recombination and intergenomic transfer. In gymnosperms only ∼20 mitogenomes have been released thus far, which hinders a systematic investigation into the tempo and mode of mitochondrial DNA evolution in seed plants. Here, we report the complete mitogenome sequence of Platycladus orientalis (Cupressaceae). This mitogenome is assembled as two circular-mapping chromosomes with a size of ∼2.6 Mb and which contains 32 protein-coding genes, three rRNA and seven tRNA genes, and 1,068 RNA editing sites. Repetitive sequences, including dispersed repeats, transposable elements (TEs), and tandem repeats, made up 23% of the genome. Comparative analyses with 17 other mitogenomes representing the five gymnosperm lineages revealed a 30-fold difference in genome size, 80-fold in repetitive content, and 230-fold in substitution rate. We found dispersed repeats are highly associated with mitogenome expansion (r = 0.99), and most of them were accumulated during recent duplication events. Syntenic blocks and shared sequences between mitogenomes decay rapidly with divergence time (r = 0.53), with the exceptions of Ginkgo and Cycads which retained conserved genome structure over long evolutionary time. Our phylogenetic analysis supports a sister group relationship of Cupressophytes and Gnetophytes; both groups are unique in that they lost 8–12 protein-coding genes, of which 4–7 intact genes are likely transferred to nucleus. These two clades also show accelerated and highly variable substitution rates relative to other gymnosperms. Our study highlights the dynamic and enigmatic evolution of gymnosperm mitogenomes.

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Heterozygotes for major chromosomal rearrangements such as fusions and fissions are expected to display a high level of sterility due to problems during meiosis. However, some species, especially plants and animals with holocentric chromosomes, are known to tolerate chromosomal heterozygosity even for multiple rearrangements. Here, we studied male meiotic chromosome behavior in four hybrid generations (F1-F4) between two chromosomal races of the Wood White butterfly Leptidea sinapis differentiated by at least 24 chromosomal fusions/fissions. Previous work showed that these hybrids were fertile, although their fertility was reduced as compared to crosses within chromosomal races. We demonstrate that (i) F1 hybrids are highly heterozygous with nearly all chromosomes participating in the formation of trivalents at the first meiotic division, and (ii) that from F1 to F4 the number of trivalents decreases and the number of bivalents increases. We argue that the observed process of chromosome sorting would, if continued, result in a new homozygous chromosomal race, i.e., in a new karyotype with intermediate chromosome number and, possibly, in a new incipient homoploid hybrid species. We also discuss the segregational model of karyotype evolution and the chromosomal model of homoploid hybrid speciation.

Parallel meta-analysis is a popular approach for increasing the power to detect genetic effects in genome-wide association studies across multiple cohorts. Consortia studying the genetics of behavioral phenotypes are oftentimes faced with systematic differences in phenotype measurement across cohorts, introducing heterogeneity into the meta-analysis and reducing statistical power. This study investigated integrative data analysis (IDA) as an approach for jointly modeling the phenotype across multiple datasets. We put forth a bi-factor integration model (BFIM) that provides a single common phenotype score and accounts for sources of study-specific variability in the phenotype. In order to capitalize on this modeling strategy, a phenotype reference panel was utilized as a supplemental sample with complete data on all behavioral measures. A simulation study showed that a mega-analysis of genetic variant effects in a BFIM were more powerful than meta-analysis of genetic effects on a cohort-specific sum score of items. Saving the factor scores from the BFIM and using those as the outcome in meta-analysis was also more powerful than the sum score in most simulation conditions, but a small degree of bias was introduced by this approach. The reference panel was necessary to realize these power gains. An empirical demonstration used the BFIM to harmonize aggression scores in 9-year old children across the Netherlands Twin Register and the Child and Adolescent Twin Study in Sweden, providing a template for application of the BFIM to a range of different phenotypes. A supplemental data collection in the Netherlands Twin Register served as a reference panel for phenotype modeling across both cohorts. Our results indicate that model-based harmonization for the study of complex traits is a useful step within genetic consortia.

Patients with glioblastoma (GBM) have a poor outcome, but even among patients receiving the same therapies and with good prognostic factors, one can find those with exceptionally short and long survival. From the Nordic trial, which randomized GBM patients of 60 years or older between two radiotherapy arms (60 Gy or 34 Gy) or temozolomide (TMZ), we selected 59 with good prognostic factors. These selected GBM patients were equally distributed according to treatment and MGMT promoter methylation status but had long or short survival. Methylation profiling with the Illumina Infinium Methylation EPIC BeadChip arrays was performed and utilized for methylation-based CNS tumor classification, and pathway enrichment analysis of differentially methylated CpG sites (DMCs), as well as calculation of epigenetic age acceleration with three different algorithms, to compare the long and short survival groups. Samples identified by the classifier as non-GBM IDH wildtype were excluded. DMCs between long- and short-term survivors were found in patients with methylated MGMT promoter treated with TMZ (123,510), those with unmethylated MGMT treated with 60Gy radiotherapy (4,086), and with methylated MGMT promoter treated with 34Gy radiotherapy (39,649). Long-term survivors with methylated MGMT promoter treated with TMZ exhibited hypermethylation of the Wnt signaling and the platelet activation, signaling, and aggregation pathways. The joint analysis of radiotherapy arms revealed 319 DMCs between long- and short-term survivors with unmethylated MGMT and none for samples with methylated MGMT promoter. An analysis comparing epigenetic age acceleration between patients with long- and short-term survival across all treatment arms showed a decreased epigenetic age acceleration for the latter. We identified DMCs for both TMZ and RT-treated patients and epigenetic age acceleration as a potential prognostic marker, but further systematic analysis of larger patient cohorts is necessary for confirmation of their prognostic and/or predictive properties.

Understanding the processes that cause speciation is a key aim of evolutionary biology. Lineages or biomes that exhibit recent and rapid diversification are ideal model systems for determining these processes. Species rich biomes reported to be of relatively recent origin, i.e., since the beginning of the Miocene, include Mediterranean ecosystems such as the California Floristic Province, oceanic islands such as the Hawaiian archipelago and the Neotropical high elevation ecosystem of the Páramos. Páramos constitute grasslands above the forest tree-line (at elevations of c. 2800–4700 m) with high species endemism. Organisms that occupy this ecosystem are a likely product of unique adaptations to an extreme environment that evolved during the last three to five million years when the Andes reached an altitude that was capable of sustaining this type of vegetation. We compared net diversification rates of lineages in fast evolving biomes using 73 dated molecular phylogenies. Based on our sample, we demonstrate that average net diversification rates of Páramo plant lineages are faster than those of other reportedly fast evolving hotspots and that the faster evolving lineages are more likely to be found in Páramos than the other hotspots. Páramos therefore represent the ideal model system for studying diversification processes. Most of the speciation events that we observed in the Páramos (144 out of 177) occurred during the Pleistocene possibly due to the effects of species range contraction and expansion that may have resulted from the well-documented climatic changes during that period. Understanding these effects will assist with efforts to determine how future climatic changes will impact plant populations.

Angioedema is a relatively rare but potentially life-threatening adverse reaction to angiotensin-converting enzyme inhibitors (ACEi) and angiotensin receptor blockers (ARBs). As with hereditary forms of angioedema (HAE), this adverse reaction is mediated by bradykinin. Research suggests that ACEi/ARB-induced angioedema has a multifactorial etiology. In addition, recent case reports suggest that some ACEi/ARB-induced angioedema patients may carry pathogenic HAE variants. The aim of the present study was to investigate the possible association between ACEi/ARB-induced angioedema and HAE genes via systematic molecular genetic screening in a large cohort of ACEi/ARB-induced angioedema cases. Targeted re-sequencing of five HAE-associated genes (SERPING1, F12, PLG, ANGPT1, and KNG1) was performed in 212 ACEi/ARB-induced angioedema patients recruited in Germany/Austria, Sweden, and Denmark, and in 352 controls from a German cohort. Among patients, none of the identified variants represented a known pathogenic variant for HAE. Moreover, no significant association with ACEi/ARB-induced angioedema was found for any of the identified common [minor allele frequency (MAF) >5%] or rare (MAF < 5%) variants. However, several non-significant trends suggestive of possible protective effects were observed. The lowest p-value for an individual variant was found in PLG (rs4252129, p.R523W, p = 0.057, p.adjust > 0.999, Fisher's exact test). Variant p.R523W was found exclusively in controls and has previously been associated with decreased levels of plasminogen, a precursor of plasmin which is part of a pathway directly involved in bradykinin production. In addition, rare, potentially functional variants (MAF < 5%, Phred-scaled combined annotation dependent depletion score >10) showed a nominally significant enrichment in controls both: 1) across all five genes; and 2) in the F12 gene alone. However, these results did not withstand correction for multiple testing. In conclusion, our results suggest that HAE-associated mutations are, at best, a rare cause of ACEi/ARB-induced angioedema. Furthermore, we were unable to identify a significant association between ACEi/ARB-induced angioedema and other variants in the investigated genes. Further studies with larger sample sizes are warranted to draw more definite conclusions concerning variants with limited effect sizes, including protective variants.

Multiple Sclerosis (MS) is an autoimmune, neurological disease, commonly presenting with a relapsing-remitting form, that later converts to a secondary progressive stage, referred to as RRMS and SPMS, respectively. Early treatment slows disease progression, hence, accurate and early diagnosis is crucial. Recent advances in large-scale data processing and analysis have progressed molecular biomarker development. Here, we focus on small RNA data derived from cell-free cerebrospinal fluid (CSF), cerebrospinal fluid cells, plasma and peripheral blood mononuclear cells as well as CSF cell methylome data, from people with RRMS (n = 20), clinically/radiologically isolated syndrome (CIS/RIS, n = 2) and neurological disease controls (n = 14). We applied multiple co-inertia analysis (MCIA), an unsupervised and thereby unbiased, multivariate method for simultaneous data integration and found that the top latent variable classifies RRMS status with an Area Under the Receiver Operating Characteristics (AUROC) score of 0.82. Variable selection based on Lasso regression reduced features to 44, derived from the small RNAs from plasma (20), CSF cells (8) and cell-free CSF (16), with a marginal reduction in AUROC to 0.79. Samples from SPMS patients (n = 6) were subsequently projected on the latent space and differed significantly from RRMS and controls. On contrary, we found no differences between relapse and remission or between inflammatory and non-inflammatory disease controls, suggesting that the latent variable is not prone to inflammatory signals alone, but could be MS-specific. Hence, we here showcase that integration of small RNAs from plasma and CSF can be utilized to distinguish RRMS from SPMS and neurological disease controls.

In eukaryotic cells, gene expression is highly regulated at many layers. Nascent RNA molecules are assembled into ribonucleoprotein complexes that are then released into the nucleoplasmic milieu and transferred to the nuclear pore complex for nuclear export. RNAs are then either translated or transported to the cellular periphery. Emerging evidence indicates that RNA-binding proteins play an essential role throughout RNA biogenesis, from the gene to polyribosomes. However, the sorting mechanisms that regulate whether an RNA molecule is immediately translated or sent to specialized locations for translation are unclear. This question is highly relevant during development and differentiation when cells acquire a specific identity. Here, we focus on the RNA-binding properties of heterogeneous nuclear ribonucleoproteins (hnRNPs) and how these mechanisms are believed to play an essential role in RNA trafficking in polarized cells. Further, by focusing on the specific hnRNP protein CBF-A/hnRNPab and its naturally occurring isoforms, we propose a model on how hnRNP proteins are capable of regulating gene expression both spatially and temporally throughout the RNA biogenesis pathway, impacting both healthy and diseased cells.

Breast cancer (BC) is the leading cause of death from malignant neoplasms among women worldwide, and metastatic BC presents the biggest problems for treatment. Previously, it was shown that lower expression of ELOVL5 and IGFBP6 genes is associated with a higher risk of the formation of distant metastases in BC. In this work, we studied the change in phenotypical traits, as well as in the transcriptomic and proteomic profiles of BC cells as a result of the stable knockdown of ELOVL5 and IGFBP6 genes. The knockdown of ELOVL5 and IGFBP6 genes was found to lead to a strong increase in the expression of the matrix metalloproteinase (MMP) MMP1. These results were in good agreement with the correlation analysis of gene expression in tumor samples from patients and were additionally confirmed by zymography. The knockdown of ELOVL5 and IGFBP6 genes was also discovered to change the expression of a group of genes involved in the formation of intercellular contacts. In particular, the expression of the CDH11 gene was markedly reduced, which also complies with the correlation analysis. The spheroid formation assay showed that intercellular adhesion decreased as a result of the knockdown of the ELOVL5 and IGFBP6 genes. Thus, the obtained data indicate that malignant breast tumors with reduced expression of the ELOVL5 and IGFBP6 genes can metastasize with a higher probability due to a more efficient invasion of tumor cells.

The mechanism of environmental factors in Kashin-Beck disease (KBD) remains unknown. We aimed to identify single nucleotide polymorphisms (SNPs) and protein alterations of selenium- and T-2 toxin-responsive genes to provide new evidence of chondrocytic damage in KBD. This study sampled the cubital venous blood of 258 subjects including 129 sex-matched KBD patients and 129 healthy controls for SNP detection. We applied an additive model, a dominant model, and a recessive model to identify significant SNPs. We then used the Comparative Toxicogenomics Database (CTD) to select selenium- and T-2 toxin-responsive genes with the candidate SNP loci. Finally, immunohistochemistry was applied to verify the protein expression of candidate genes in knee cartilage obtained from 15 subjects including 5 KBD, 5 osteoarthritis (OA), and 5 healthy controls. Forty-nine SNPs were genotyped in the current study. The C allele of rs6494629 was less frequent in KBD than in the controls (OR = 0.63, p = 0.011). Based on the CTD database, PPARG, ADAM12, IL6, SMAD3, and TIMP2 were identified to interact with selenium, sodium selenite, and T-2 toxin. KBD was found to be significantly associated with rs12629751 of PPARG (additive model: OR = 0.46, p = 0.012; dominant model: OR = 0.45, p = 0.049; recessive model: OR = 0.18, p = 0.018), rs1871054 of ADAM12 (dominant model: OR = 2.19, p = 0.022), rs1800796 of IL6 (dominant model: OR = 0.30, p = 0.003), rs6494629 of SMAD3 (additive model: OR = 0.65, p = 0.019; dominant model: OR = 0.52, p = 0.012), and rs4789936 of TIMP2 (recessive model: OR = 5.90, p = 0.024). Immunohistochemistry verified significantly upregulated PPARG, ADAM12, SMAD3, and TIMP2 in KBD compared with OA and normal controls (p < 0.05). Genetic polymorphisms of PPARG, ADAM12, SMAD3, and TIMP2 may contribute to the risk of KBD. These genes could promote the pathogenesis of KBD by disturbing ECM homeostasis.