Browsing by Subject "PACKAGE"

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  • Gabriel, Michael; Fey, Vidal; Heinosalo, Taija; Adhikari, Prem; Rytkönen, Kalle; Komulainen, Tuomo; Huhtinen, Kaisa; Laajala, Teemu Daniel; Siitari, Harri; Virkki, Arho; Suvitie, Pia; Kujari, Harry; Aittokallio, Tero; Perheentupa, Antti; Poutanen, Matti (2020)
    Endometriosis is a common inflammatory estrogen-dependent gynecological disorder, associated with pelvic pain and reduced fertility in women. Several aspects of this disorder and its cellular and molecular etiology remain unresolved. We have analyzed the global gene expression patterns in the endometrium, peritoneum and in endometriosis lesions of endometriosis patients and in the endometrium and peritoneum of healthy women. In this report, we present the EndometDB, an interactive web-based user interface for browsing the gene expression database of collected samples without the need for computational skills. The EndometDB incorporates the expression data from 115 patients and 53 controls, with over 24000 genes and clinical features, such as their age, disease stages, hormonal medication, menstrual cycle phase, and the different endometriosis lesion types. Using the web-tool, the end-user can easily generate various plot outputs and projections, including boxplots, and heatmaps and the generated outputs can be downloaded in pdf-format.
  • Rekker, Kadri; Altmae, Signe; Suhorutshenko, Marina; Peters, Maire; Martinez-Blanch, Juan F.; Codoner, Francisco M.; Vilella, Felipe; Simon, Carlos; Salumets, Andres; Velthut-Meikas, Agne (2018)
    The endometrium undergoes extensive changes to prepare for embryo implantation and microRNAs (miRNAs) have been described as playing a significant role in the regulation of endometrial receptivity. However, there is no consensus about the miRNAs involved in mid-secretory endometrial functions. We analysed the complete endometrial miRNome from early secretory (pre-receptive) and mid-secretory (receptive) phases from fertile women and from patients with recurrent implantation failure (RIF) to reveal differentially expressed (DE) miRNAs in the mid-secretory endometrium. Furthermore, we investigated whether the overall changes during early to mid-secretory phase transition and with RIF condition could be reflected in blood miRNA profiles. In total, 116 endometrial and 114 matched blood samples collected from two different population cohorts were subjected to small RNA sequencing. Among fertile women, 91 DE miRNAs were identified in the mid-secretory vs. early secretory endometrium, while no differences were found in the corresponding blood samples. The comparison of mid-secretory phase samples between fertile and infertile women revealed 21 DE miRNAs from the endometrium and one from blood samples. Among discovered novel miRNAs, chr2_4401 was validated and showed up-regulation in the mid-secretory endometrium. Besides novel findings, we confirmed the involvement of miR-30 and miR-200 family members in mid-secretory endometrial functions.
  • Neumann, Alexander; Walton, Esther; Alemany, Silvia; Cecil, Charlotte; Gonzalez, Juan Ramon; Jima, Dereje D.; Lahti, Jari; Tuominen, Samuli T.; Barker, Edward D.; Binder, Elisabeth; Caramaschi, Doretta; Carracedo, Angel; Czamara, Darina; Evandt, Jorunn; Felix, Janine F.; Fuemmeler, Bernard F.; Gutzkow, Kristine B.; Hoyo, Cathrine; Julvez, Jordi; Kajantie, Eero; Laivuori, Hannele; Maguire, Rachel; Maitre, Lea; Murphy, Susan K.; Murcia, Mario; Villa, Pia M.; Sharp, Gemma; Sunyer, Jordi; Räikkönen, Katri; Bakermans-Kranenburg, Marian; van Ijzendoorn, Marinus; Guxens, Monica; Relton, Caroline L.; Tiemeier, Henning (2020)
    Attention-deficit and hyperactivity disorder (ADHD) is a common childhood disorder with a substantial genetic component. However, the extent to which epigenetic mechanisms play a role in the etiology of the disorder is unknown. We performed epigenome-wide association studies (EWAS) within the Pregnancy And Childhood Epigenetics (PACE) Consortium to identify DNA methylation sites associated with ADHD symptoms at two methylation assessment periods: birth and school age. We examined associations of both DNA methylation in cord blood with repeatedly assessed ADHD symptoms (age 4-15 years) in 2477 children from 5 cohorts and of DNA methylation at school age with concurrent ADHD symptoms (age 7-11 years) in 2374 children from 9 cohorts, with 3 cohorts participating at both timepoints. CpGs identified with nominal significance (p <0.05) in either of the EWAS were correlated between timepoints (rho = 0.30), suggesting overlap in associations; however, top signals were very different. At birth, we identified nine CpGs that predicted later ADHD symptoms (p <1 x 10(-7)), including ERC2 and CREB5. Peripheral blood DNA methylation at one of these CpGs (cg01271805 in the promoter region of ERC2, which regulates neurotransmitter release) was previously associated with brain methylation. Another (cg25520701) lies within the gene body of CREB5, which previously was associated with neurite outgrowth and an ADHD diagnosis. In contrast, at school age, no CpGs were associated with ADHD with p <1 x 10(-7). In conclusion, we found evidence in this study that DNA methylation at birth is associated with ADHD. Future studies are needed to confirm the utility of methylation variation as biomarker and its involvement in causal pathways.
  • Hanif, Tanzeela; Dhaygude, Kishor; Kankainen, Matti; Renkonen, Jutta; Mattila, Pirkko; Ojala, Teija; Joenvaara, Sakari; Mäkelä, Mika; Pelkonen, Anna; Kauppi, Paula; Haahtela, Tari; Renkonen, Risto; Toppila-Salmi, Sanna (2019)
  • Das Roy, Rishi; Hallikas, Outi; Christensen, Mona M.; Renvoise, Elodie; Jernvall, Jukka; Morozov, Alexandre V.; Roy, Sushmita; Morozov, Alexandre V.; Roy, Sushmita; Morozov, Alexandre V.; Roy, Sushmita (2021)
    Author summary Development of organs is typically associated with small and hard to detect changes in gene expression. Here we examined how often genes regulating specific organs are neighbours to each other in the genome, and whether this gene neighbourhood helps in the detection of changes in gene expression. We found that genes regulating individual organ development are very rarely close to each other in the mouse and human genomes. We built an algorithm, called DELocal, to detect changes in gene expression that incorporates information about neighbouring genes. Using transcriptomes of developing mouse molar teeth containing gene expression profiles of thousands of genes, we show how genes regulating tooth development are ranked high by DELocal even if their expression level changes are subtle. We propose that developmental biology studies can benefit from gene neighbourhood analyses in the detection of differential expression and identification of organ specific genes. Although most genes share their chromosomal neighbourhood with other genes, distribution of genes has not been explored in the context of individual organ development; the common focus of developmental biology studies. Because developmental processes are often associated with initially subtle changes in gene expression, here we explored whether neighbouring genes are informative in the identification of differentially expressed genes. First, we quantified the chromosomal neighbourhood patterns of genes having related functional roles in the mammalian genome. Although the majority of protein coding genes have at least five neighbours within 1 Mb window around each gene, very few of these neighbours regulate development of the same organ. Analyses of transcriptomes of developing mouse molar teeth revealed that whereas expression of genes regulating tooth development changes, their neighbouring genes show no marked changes, irrespective of their level of expression. Finally, we test whether inclusion of gene neighbourhood in the analyses of differential expression could provide additional benefits. For the analyses, we developed an algorithm, called DELocal that identifies differentially expressed genes by comparing their expression changes to changes in adjacent genes in their chromosomal regions. Our results show that DELocal removes detection bias towards large changes in expression, thereby allowing identification of even subtle changes in development. Future studies, including the detection of differential expression, may benefit from, and further characterize the significance of gene-gene neighbour relationships.
  • IDG-DREAM Drug-Kinase Binding; Cichonska, Anna; Ravikumar, Balaguru; Tanoli, Ziaurrehman; Aittokallio, Tero (2021)
    Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts.
  • Yohannes, Dawit A.; Freitag, Tobias L.; de Kauwe, Andrea; Kaukinen, Katri; Kurppa, Kalle; Wacklin, Pirjo; Mäki, Markku; Arstila, T. Petteri; Anderson, Robert P.; Greco, Dario; Saavalainen, Päivi (2017)
    Celiac disease (CD) patients mount an abnormal immune response to gluten. T-cell receptor (TCR) repertoires directed to some immunodominant gluten peptides have previously been described, but the global immune response to in vivo gluten exposure in CD has not been systematically investigated yet. Here, we characterized signatures associated with gluten directed immune activity and identified gluten-induced T-cell clonotypes from total blood and gut TCR repertoires in an unbiased manner using immunosequencing. CD patient total TCR repertoires showed increased overlap and substantially altered TRBV-gene usage in both blood and gut samples, and increased diversity in the gut during gluten exposure. Using differential abundance analysis, we identified gluten-induced clonotypes in each patient that were composed of a large private and an important public component. Hierarchical clustering of public clonotypes associated with dietary gluten exposure identified subsets of highly similar clonotypes, the most proliferative of which showing significant enrichment for the motif ASS[LF] R[SW][TD][DT][TE][QA][YF] in PBMC repertoires. These results show that CD-associated clonotypes can be identified and that common gluten associated immune response features can be characterized in vivo from total repertoires, with potential use in disease stratification and monitoring.
  • Feng, Shaohong; Stiller, Josefin; Deng, Yuan; Armstrong, Joel; Fang, Qi; Reeve, Andrew Hart; Xie, Duo; Chen, Guangji; Guo, Chunxue; Faircloth, Brant C.; Petersen, Bent; Wang, Zongji; Zhou, Qi; Diekhans, Mark; Chen, Wanjun; Andreu-Sanchez, Sergio; Margaryan, Ashot; Howard, Jason Travis; Parent, Carole; Pacheco, George; Sinding, Mikkel-Holger S.; Puetz, Lara; Cavill, Emily; Ribeiro, Angela M.; Eckhart, Leopold; Fjeldsa, Jon; Hosner, Peter A.; Brumfield, Robb T.; Christidis, Les; Bertelsen, Mads F.; Sicheritz-Ponten, Thomas; Tietze, Dieter Thomas; Robertson, Bruce C.; Song, Gang; Borgia, Gerald; Claramunt, Santiago; Lovette, Irby J.; Cowen, Saul J.; Njoroge, Peter; Dumbacher, John Philip; Ryder, Oliver A.; Fuchs, Jerome; Bunce, Michael; Burt, David W.; Cracraft, Joel; Meng, Guanliang; Hackett, Shannon J.; Ryan, Peter G.; Jønsson, Knud Andreas; Jamieson, Ian G.; da Fonseca, Rute R.; Braun, Edward L.; Houde, Peter; Mirarab, Siavash; Suh, Alexander; Hansson, Bengt; Ponnikas, Suvi; Sigeman, Hanna; Stervander, Martin; Frandsen, Paul B.; van der Zwan, Henriette; van der Sluis, Rencia; Visser, Carina; Balakrishnan, Christopher N.; Clark, Andrew G.; Fitzpatrick, John W.; Bowman, Reed; Chen, Nancy; Cloutier, Alison; Sackton, Timothy B.; Edwards, Scott V.; Foote, Dustin J.; Shakya, Subir B.; Sheldon, Frederick H.; Vignal, Alain; Soares, Andre E. R.; Shapiro, Beth; Gonzalez-Solis, Jacob; Ferrer-Obiol, Joan; Rozas, Julio; Riutort, Marta; Tigano, Anna; Friesen, Vicki; Dalen, Love; Urrutia, Araxi O.; Szekely, Tamas; Liu, Yang; Campana, Michael G.; Corvelo, Andre; Fleischer, Robert C.; Rutherford, Kim M.; Gemmell, Neil J.; Dussex, Nicolas; Mouritsen, Henrik; Thiele, Nadine; Delmore, Kira; Liedvogel, Miriam; Franke, Andre; Hoeppner, Marc P.; Krone, Oliver; Fudickar, Adam M.; Mila, Borja; Ketterson, Ellen D.; Fidler, Andrew Eric; Friis, Guillermo; Parody-Merino, Angela M.; Battley, Phil F.; Cox, Murray P.; Lima, Nicholas Costa Barroso; Prosdocimi, Francisco; Parchman, Thomas Lee; Schlinger, Barney A.; Loiselle, Bette A.; Blake, John G.; Lim, Haw Chuan; Day, Lainy B.; Fuxjager, Matthew J.; Baldwin, Maude W.; Braun, Michael J.; Wirthlin, Morgan; Dikow, Rebecca B.; Ryder, T. Brandt; Camenisch, Glauco; Keller, Lukas F.; DaCosta, Jeffrey M.; Hauber, Mark E.; Louder, Matthew I. M.; Witt, Christopher C.; McGuire, Jimmy A.; Mudge, Joann; Megna, Libby C.; Carling, Matthew D.; Wang, Biao; Taylor, Scott A.; Del-Rio, Glaucia; Aleixo, Alexandre; Vasconcelos, Ana Tereza Ribeiro; Mello, Claudio V.; Weir, Jason T.; Haussler, David; Li, Qiye; Yang, Huanming; Wang, Jian; Lei, Fumin; Rahbek, Carsten; Gilbert, M. Thomas P.; Graves, Gary R.; Jarvis, Erich D.; Paten, Benedict; Zhang, Guojie (2020)
    Whole-genome sequencing projects are increasingly populating the tree of life and characterizing biodiversity(1-4). Sparse taxon sampling has previously been proposed to confound phylogenetic inference(5), and captures only a fraction of the genomic diversity. Here we report a substantial step towards the dense representation of avian phylogenetic and molecular diversity, by analysing 363 genomes from 92.4% of bird families-including 267 newly sequenced genomes produced for phase II of the Bird 10,000 Genomes (B10K) Project. We use this comparative genome dataset in combination with a pipeline that leverages a reference-free whole-genome alignment to identify orthologous regions in greater numbers than has previously been possible and to recognize genomic novelties in particular bird lineages. The densely sampled alignment provides a single-base-pair map of selection, has more than doubled the fraction of bases that are confidently predicted to be under conservation and reveals extensive patterns of weak selection in predominantly non-coding DNA. Our results demonstrate that increasing the diversity of genomes used in comparative studies can reveal more shared and lineage-specific variation, and improve the investigation of genomic characteristics. We anticipate that this genomic resource will offer new perspectives on evolutionary processes in cross-species comparative analyses and assist in efforts to conserve species. A dataset of the genomes of 363 species from the Bird 10,000 Genomes Project shows increased power to detect shared and lineage-specific variation, demonstrating the importance of phylogenetically diverse taxon sampling in whole-genome sequencing.
  • Lauter, Gilbert; Coschiera, Andrea; Yoshihara, Masahito; Sugiaman-Trapman, Debora; Ezer, Sini; Sethurathinam, Shalini; Katayama, Shintaro; Kere, Juha; Swoboda, Peter (2020)
    Many human cell types are ciliated, including neural progenitors and differentiated neurons. Ciliopathies are characterized by defective cilia and comprise various disease states, including brain phenotypes, where the underlying biological pathways are largely unknown. Our understanding of neuronal cilia is rudimentary, and an easy-to-maintain, ciliated human neuronal cell model is absent. The Lund human mesencephalic (LUHMES) cell line is a ciliated neuronal cell line derived from human fetal mesencephalon. LUHMES cells can easily be maintained and differentiated into mature, functional neurons within one week. They have a single primary cilium as proliferating progenitor cells and as postmitotic, differentiating neurons. These developmental stages are completely separable within one day of culture condition change. The sonic hedgehog (SHH) signaling pathway is active in differentiating LUHMES neurons. RNA-sequencing imecourse analyses reveal molecular pathways and gene-regulatory networks critical for ciliogenesis and axon outgrowth at the interface between progenitor cell proliferation, polarization and neuronal differentiation. Gene expression dynamics of cultured LUHMES neurons faithfully mimic the corresponding in vivo dynamics of human fetal midbrain. In LUHMES cells, neuronal cilia biology can be investigated from proliferation through differentiation to mature neurons.
  • Gruzieva, Olena; Merid, Simon Kebede; Chen, Su; Mukherjee, Nandini; Hedman, Anna M.; Almqvist, Catarina; Andolf, Ellika; Jiang, Yu; Kere, Juha; Scheynius, Annika; Soderhall, Cilia; Ullemar, Vilhelmina; Karmaus, Wilfried; Melen, Erik; Arshad, Syed Hasan; Pershagen, Goran (2019)
    There is emerging evidence on DNA methylation (DNAm) variability over time; however, little is known about dynamics of DNAm patterns during pregnancy. We performed an epigenome-wide longitudinal DNAm study of a well-characterized sample of young women from the Swedish Born into Life study, with repeated blood sampling before, during and after pregnancy (n = 21), using the Illumina Infinium MethylationEPIC array. We conducted a replication in the Isle of Wight third-generation birth cohort (n = 27), using the Infinium HumanMethylation450k BeadChip. We identified 196 CpG sites displaying intra-individual longitudinal change in DNAm with a false discovery rate (FDR) P <.05. Most of these (91%) showed a decrease in average methylation levels over the studied period. We observed several genes represented by > 3 differentially methylated CpGs: HOXB3, AVP, LOC100996291, and MicroRNA 10a. Of 36 CpGs available in the replication cohort, 17 were replicated, all but 2 with the same direction of association (replication P <.05). Biological pathway analysis demonstrated that FDR-significant CpGs belong to genes overrepresented in metabolism-related pathways, such as adipose tissue development, regulation of insulin receptor signaling, and mammary gland fat development. These results contribute to a better understanding of the biological mechanisms underlying important physiological alterations and adaptations for pregnancy and lactation.
  • Andrews, Caitlin E.; Ewen, John G.; Thorogood, Rose (2020)
    Studies of intraspecific dietary variation can greatly enrich our view of a species' niche and role in the ecosystem, particularly when species with broad diets are found to be composed of generalist and specialist individuals. However, the current framework for quantifying dietary specialization leaves certain standards unformalized and is susceptible to overestimating specialization when there are few repeated observations per individual, as is often the case in observational studies of wild populations. Here, we use the hihi (Notiomystis cincta), a threatened New Zealand passerine, as a case study for demonstrating how existing statistical tools can be applied to strengthen the dietary specialization framework. First, we assess whether the reliability of common dietary measures can be improved through Bayesian adjustments and by using rarefaction to compare uncertainty levels of metrics calculated from different sample sizes. As diet links closely to environmental factors, we also demonstrate how adding phenological data and habitat assessments to standard protocols can help validate our dietary measures as evidence for resource selection rather than random foraging. Finally, in light of our finding that diet predicts survival in hihi, we discuss the utility of dietary specialization for elucidating broader behavioral syndromes.
  • Alvarez, Marcus; Rahmani, Elior; Jew, Brandon; Garske, Kristina M.; Miao, Zong; Benhammou, Jihane N.; Ye, Chun Jimmie; Pisegna, Joseph R.; Pietiläinen, Kirsi H.; Halperin, Eran; Pajukanta, Paivi (2020)
    Single-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. We observe that snRNA-seq is commonly subject to contamination by high amounts of ambient RNA, which can lead to biased downstream analyses, such as identification of spurious cell types if overlooked. We present a novel approach to quantify contamination and filter droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM). Our likelihood-based approach models the gene expression distribution of debris and cell types, which are estimated using EM. We evaluated DIEM using three snRNA-seq data sets: (1) human differentiating preadipocytes in vitro, (2) fresh mouse brain tissue, and (3) human frozen adipose tissue (AT) from six individuals. All three data sets showed evidence of extranuclear RNA contamination, and we observed that existing methods fail to account for contaminated droplets and led to spurious cell types. When compared to filtering using these state of the art methods, DIEM better removed droplets containing high levels of extranuclear RNA and led to higher quality clusters. Although DIEM was designed for snRNA-seq, our clustering strategy also successfully filtered single-cell RNA-seq data. To conclude, our novel method DIEM removes debris-contaminated droplets from single-cell-based data fast and effectively, leading to cleaner downstream analysis. Our code is freely available for use at https://github.com/marcalva/diem.
  • Marwah, Veer Singh; Scala, Giovanni; Kinaret, Pia Anneli Sofia; Serra, Angela; Alenius, Harri; Fortino, Vittorio; Greco, Dario (2019)
    BackgroundApplication of microarrays in omics technologies enables quantification of many biomolecules simultaneously. It is widely applied to observe the positive or negative effect on biomolecule activity in perturbed versus the steady state by quantitative comparison. Community resources, such as Bioconductor and CRAN, host tools based on R language that have become standard for high-throughput analytics. However, application of these tools is technically challenging for generic users and require specific computational skills. There is a need for intuitive and easy-to-use platform to process omics data, visualize, and interpret results.ResultsWe propose an integrated software solution, eUTOPIA, that implements a set of essential processing steps as a guided workflow presented to the user as an R Shiny application.ConclusionseUTOPIA allows researchers to perform preprocessing and analysis of microarray data via a simple and intuitive graphical interface while using state of the art methods.
  • Frelat, Romain; Kortsch, Susanne; Kroencke, Ingrid; Neumann, Hermann; Nordstroem, Marie C.; Olivier, Pierre E. N.; Sell, Anne F. (2022)
    Ecological communities are constantly changing as a response to environmental and anthropogenic pressures. Yet, how changes in community composition influence the structure of food webs over time and space remains elusive. Using ecological network analysis, we assessed how food web structure changed across six distinct areas of the North Sea over a sixteen-year time-period. We used multivariate analyses to disentangle and compare spatio-temporal dynamics in community composition (i.e. changes in species abundances) and food web structure (i.e. changes in network properties). Specifically, we assessed how changes in community composition were reflected in food web structure. Our results revealed a strong spatial coupling between community composition and food web structure along a south-north gradient. However, the temporal covariation between community composition and food web structure depended on the spatial scale. We observed a temporal mismatch at regional scale, but a strong coupling at local scale. In particular, we found that community composition can be influenced by hydro-climatic events over large areas, with diverse effects manifesting in local food web structure. Our proposed methodological framework quantified and compared spatio-temporal changes in community composition and food web structure, providing key information to support effective management strategies aimed at conserving the structure and functioning of ecological communities in times of environmental change.
  • Mallawaarachchi, Sudaraka; Tonkin-Hill, Gerry; Croucher, Nicholas J.; Turner, Paul; Speed, Doug; Corander, Jukka; Balding, David (2022)
    Whole-genome sequencing has facilitated genome-wide analyses of association, prediction and heritability in many organisms. However, such analyses in bacteria are still in their infancy, being limited by difficulties including genome plasticity and strong population structure. Here we propose a suite of methods including linear mixed models, elastic net and LD-score regression, adapted to bacterial traits using innovations such as frequency-based allele coding, both insertion/deletion and nucleotide testing and heritability partitioning. We compare and validate our methods against the current state-of-art using simulations, and analyse three phenotypes of the major human pathogen Streptococcus pneumoniae, including the first analyses of minimum inhibitory concentrations (MIC) for penicillin and ceftriaxone. We show that the MIC traits are highly heritable with high prediction accuracy, explained by many genetic associations under good population structure control. In ceftriaxone MIC, this is surprising because none of the isolates are resistant as per the inhibition zone criteria. We estimate that half of the heritability of penicillin MIC is explained by a known drug-resistance region, which also contributes a quarter of the ceftriaxone MIC heritability. For the within-host carriage duration phenotype, no associations were observed, but the moderate heritability and prediction accuracy indicate a moderately polygenic trait.
  • Luukkonen, Panu K.; Tukiainen, Taru; Juuti, Anne; Sammalkorpi, Henna; Haridas, P. A. Nidhina; Niemelä, Onni; Arola, Johanna; Orho-Melander, Marju; Hakkarainen, Antti; Kovanen, Petri T.; Dwivedi, Om; Groop, Leif; Hodson, Leanne; Gastaldelli, Amalia; Hyötyläinen, Tuulia; Oresic, Matej; Yki-Järvinen, Hannele (2020)
    Carriers of the hydroxysteroid 17-beta dehydrogenase 13 (HSD17B13) gene variant (rs72613567:TA) have a reduced risk of NASH and cirrhosis but not steatosis. We determined its effect on liver histology, lipidome, and transcriptome using ultra performance liquid chromatography-mass spectrometry and RNA-seq. In carriers and noncarriers of the gene variant, we also measured pathways of hepatic fatty acids (de novo lipogenesis [ONLI and adipose tissue lipolysis [ATL] using (H2O)-H-2 and H-2-glycerol) and insulin sensitivity using H-3-glucose and euglycemic-hyperinsulinemic clamp) and plasma cytokines. Carriers and noncarriers had similar age, sex and BMI. Fibrosis was significantly less frequent while phospholipids, but not other lipids, were enriched in the liver in carriers compared with noncarriers. Expression of 274 genes was altered in carriers compared with noncarriers, consisting predominantly of downregulated inflammation-related gene sets. Plasma IL-6 concentrations were lower, but DNL, ATL and hepatic insulin sensitivity were similar between the groups. In conclusion, carriers of the HSD17B13 variant have decreased fibrosis and expression of inflammation-related genes but increased phospholipids in the liver. These changes are not secondary to steatosis, ONL, ATL, or hepatic insulin sensitivity. The increase in phospholipids and decrease in fibrosis are opposite to features of choline-deficient models of liver disease and suggest HSD17B13 as an attractive therapeutic target.
  • Federico, Antonio; Fratello, Michele; Scala, Giovanni; Möbus, Lena; Pavel, Alisa; del Giudice, Giusy; Ceccarelli, Michele; Costa, Valerio; Ciccodicola, Alfredo; Fortino, Vittorio; Serra, Angela; Greco, Dario (2022)
    Simple Summary Current treatments for complex diseases, including cancer, are generally characterized by high toxicity due to their low selectivity for target cells. Moreover, patients often develop drug resistance, hence becoming less sensitive to the therapy. For this reason, novel, improved, and more specific pharmacological therapies are needed. The high cost and the time required to develop new drugs poses the attention on the development of computational methods for drug repositioning and combination therapy prediction. In this study, we developed an integrated network pharmacology framework that combines mechanistic and chemocentric approaches in order to predict potential drug combinations for cancer therapy. We applied our paradigm in five cancer types, which we used as case studies. Our strategy can be applied to the study of any complex disease by guiding the prioritization of drug combinations. Despite remarkable efforts of computational and predictive pharmacology to improve therapeutic strategies for complex diseases, only in a few cases have the predictions been eventually employed in the clinics. One of the reasons behind this drawback is that current predictive approaches are based only on the integration of molecular perturbation of a certain disease with drug sensitivity signatures, neglecting intrinsic properties of the drugs. Here we integrate mechanistic and chemocentric approaches to drug repositioning by developing an innovative network pharmacology strategy. We developed a multilayer network-based computational framework integrating perturbational signatures of the disease as well as intrinsic characteristics of the drugs, such as their mechanism of action and chemical structure. We present five case studies carried out on public data from The Cancer Genome Atlas, including invasive breast cancer, colon adenocarcinoma, lung squamous cell carcinoma, hepatocellular carcinoma and prostate adenocarcinoma. Our results highlight paclitaxel as a suitable drug for combination therapy for many of the considered cancer types. In addition, several non-cancer-related genes representing unusual drug targets were identified as potential candidates for pharmacological treatment of cancer.
  • Scala, Giovanni; Marwah, Veer; Kinaret, Pia; Sund, Jukka; Fortino, Vittorio; Greco, Dario (2018)
    We present data derived from an exposure experiment in which three cell-lines representative of cell types of the respiratory tissue (epithelial type-I A549, epithelial type-II BEAS-2B, and macrophage THP-1) have been exposed to ten different carbon-based nanomaterials for 48 h. In particular, we provide: genome-wide mRNA and miRNA expression, and DNA methylation; gene tables, containing information on the aberrations induced in these three genomic data layers at the gene level; mechanism of action (MOA) maps representing the comparative functional alteration induced in each cell line and each exposure. (C) 2018 Published by Elsevier Inc.
  • Miettinen, Teemu; Nieminen, Anni I.; Mäntyselkä, Pekka; Kalso, Eija; Lotsch, Jorn (2022)
    Recent scientific evidence suggests that chronic pain phenotypes are reflected in metabolomic changes. However, problems associated with chronic pain, such as sleep disorders or obesity, may complicate the metabolome pattern. Such a complex phenotype was investigated to identify common metabolomics markers at the interface of persistent pain, sleep, and obesity in 71 men and 122 women undergoing tertiary pain care. They were examined for patterns in d = 97 metabolomic markers that segregated patients with a relatively benign pain phenotype (low and little bothersome pain) from those with more severe clinical symptoms (high pain intensity, more bothersome pain, and co-occurring problems such as sleep disturbance). Two independent lines of data analysis were pursued. First, a data-driven supervised machine learning-based approach was used to identify the most informative metabolic markers for complex phenotype assignment. This pointed primarily at adenosine monophosphate (AMP), asparagine, deoxycytidine, glucuronic acid, and propionylcarnitine, and secondarily at cysteine and nicotinamide adenine dinucleotide (NAD) as informative for assigning patients to clinical pain phenotypes. After this, a hypothesis-driven analysis of metabolic pathways was performed, including sleep and obesity. In both the first and second line of analysis, three metabolic markers (NAD, AMP, and cysteine) were found to be relevant, including metabolic pathway analysis in obesity, associated with changes in amino acid metabolism, and sleep problems, associated with downregulated methionine metabolism. Taken together, present findings provide evidence that metabolomic changes associated with co-occurring problems may play a role in the development of severe pain. Co-occurring problems may influence each other at the metabolomic level. Because the methionine and glutathione metabolic pathways are physiologically linked, sleep problems appear to be associated with the first metabolic pathway, whereas obesity may be associated with the second.
  • Kupers, Leanne K.; Monnereau, Claire; Sharp, Gemma C.; Yousefi, Paul; Salas, Lucas A.; Ghantous, Akram; Page, Christian M.; Reese, Sarah E.; Wilcox, Allen J.; Czamara, Darina; Starling, Anne P.; Novoloaca, Alexei; Lent, Samantha; Roy, Ritu; Hoyo, Cathrine; Breton, Carrie; Allard, Catherine; Just, Allan C.; Bakulski, Kelly M.; Holloway, John W.; Everson, Todd M.; Xu, Cheng-Jian; Huang, Rae-Chi; van der Plaat, Diana A.; Wielscher, Matthias; Merid, Simon Kebede; Ullemar, Vilhelmina; Rezwan, Faisal; Lahti, Jari; van Dongen, Jenny; Langie, Sabine A. S.; Richardson, Tom G.; Magnus, Maria C.; Nohr, Ellen A.; Xu, Zongli; Duijts, Liesbeth; Zhao, Shanshan; Zhang, Weiming; Plusquin, Michelle; DeMeo, Dawn L.; Solomon, Olivia; Heimovaara, Joosje H.; Jima, Dereje D.; Gao, Lu; Bustamante, Mariona; Perron, Patrice; Wright, Robert O.; Hertz-Picciotto, Irva; Zhang, Hongmei; Karagas, Margaret R.; Gehring, Ulrike; Marsit, Carmen J.; Beilin, Lawrence J.; Vonk, Judith M.; Jarvelin, Marjo-Riitta; Bergstrom, Anna; Ortqvist, Anne K.; Ewart, Susan; Villa, Pia M.; Moore, Sophie E.; Willemsen, Gonneke; Standaert, Arnout R. L.; Haberg, Siri E.; Sorensen, Thorkild I. A.; Taylor, Jack A.; Räikkönen, Katri; Yang, Ivana; Kechris, Katerina; Nawrot, Tim S.; Silver, Matt J.; Gong, Yun Yun; Richiardi, Lorenzo; Kogevinas, Manolis; Litonjua, Augusto A.; Eskenazi, Brenda; Huen, Karen; Mbarek, Hamdi; Maguire, Rachel L.; Dwyer, Terence; Vrijheid, Martine; Bouchard, Luigi; Baccarelli, Andrea A.; Croen, Lisa A.; Karmaus, Wilfried; Anderson, Denise; de Vries, Maaike; Sebert, Sylvain; Kere, Juha; Karlsson, Robert; Arshad, Syed Hasan; Hämäläinen, Esa; Routledge, Michael N.; Boomsma, Dorret; Feinberg, Andrew P.; Newschaffer, Craig J.; Govarts, Eva; Moisse, Matthieu; Fallin, M. Daniele; Melen, Erik; Prentice, Andrew M.; Kajantie, Eero; Almqvist, Catarina; Oken, Emily; Dabelea, Dana; Boezen, H. Marike; Melton, Phillip E.; Wright, Rosalind J.; Koppelman, Gerard H.; Trevisi, Letizia; Hivert, Marie-France; Sunyer, Jordi; Munthe-Kaas, Monica C.; Murphy, Susan K.; Corpeleijn, Eva; Wiemels, Joseph; Holland, Nina; Herceg, Zdenko; Binder, Elisabeth B.; Smith, George Davey; Jaddoe, Vincent W. V.; Lie, Rolv T.; Nystad, Wenche; London, Stephanie J.; Lawlor, Debbie A.; Relton, Caroline L.; Snieder, Harold; Felix, Janine F. (2019)
    Birthweight is associated with health outcomes across the life course, DNA methylation may be an underlying mechanism. In this meta-analysis of epigenome-wide association studies of 8,825 neonates from 24 birth cohorts in the Pregnancy And Childhood Epigenetics Consortium, we find that DNA methylation in neonatal blood is associated with birthweight at 914 sites, with a difference in birthweight ranging from -183 to 178 grams per 10% increase in methylation (P-Bonferroni <1.06 x 10(-7)). In additional analyses in 7,278 participants,