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Left-right asymmetry is an important organizing feature of the healthy brain that may be altered in schizophrenia, but most studies have used relatively small samples and heterogeneous approaches, resulting in equivocal findings. We carried out the largest case-control study of structural brain asymmetries in schizophrenia, with MRI data from 5,080 affected individuals and 6,015 controls across 46 datasets, using a single image analysis protocol. Asymmetry indexes were calculated for global and regional cortical thickness, surface area, and subcortical volume measures. Differences of asymmetry were calculated between affected individuals and controls per dataset, and effect sizes were meta-analyzed across datasets. Small average case-control differences were observed for thickness asymmetries of the rostral anterior cingulate and the middle temporal gyrus, both driven by thinner left-hemispheric cortices in schizophrenia. Analyses of these asymmetries with respect to the use of antipsychotic medication and other clinical variables did not show any significant associations. Assessment of age- and sex-specific effects revealed a stronger average leftward asymmetry of pallidum volume between older cases and controls. Case-control differences in a multivariate context were assessed in a subset of the data (N = 2,029), which revealed that 7% of the variance across all structural asymmetries was explained by case-control status. Subtle case-control differences of brain macrostructural asymmetry may reflect differences at the molecular, cytoarchitectonic, or circuit levels that have functional relevance for the disorder. Reduced left middle temporal cortical thickness is consistent with altered left-hemisphere language network organization in schizophrenia.

This project contains a preregistration of secondary data analysis for a research project on the complex links between the genetics influence on the wellbeing spectrum, brain structure, and risk for psychopathology in children.

Additional file 1: Fig. S1. FACS-plot of D. melanogaster eye-antennal disc cells after live/dead cell staining. (A) Counterstaining of Propidium Iodide to label dead cells (y-axis, Q1) and Calcein violet to label live cells (x-axis, Q4). Double positive signals might indicate dying cells or incompletely separated cells (Q2). This method allows removing debris (Q3) efficiently. (B) The cell population P4 (i.e. 16,208 living cells) were isolated and used for the scRNAseq run using 10x Genomics. Fig. S2. Quality and quantity of cDNA after reverse transcription of mRNA fraction (polyA-based enrichment) and full-length cDNA amplification from cell lysate of 30 cells sorted from cell suspension of eye-antennal discs run on Fragment Analyzer (Agilent). Size distribution of all fragments shows little impact on degradation (almost no cDNA detectable below 400 bp). Fig. S3. Contribution of mitochondrial gene expression to scRNAseq and snRNAseq datasets. (A) Total amount of genes (features) over percentage of mitochondrial reads, per cell each. The dashed line indicates a threshold of 10% of reads attributed to mitochondrial genes. In scRNAseq data, approximately 14% of cells show a high (>10%) proportion of mitochondrial gene reads on the total number of reads. (B) Total amount of genes (features) over Percentage of mitochondrial reads, per cell each in snRNAseq data. The dashed line indicates a threshold of 10% of reads attributed to mitochondrial genes. In most nuclei, only a low percentage of reads is attributed to mitochondrial genes. Fig. S4. Clustering and cluster annotation for scRNAseq data. (A) The heatmap shows the score for each potential cell type (Y-axis) in each cluster (X-axis). The cell types are annotated based on the highest scoring identity in the heatmap. The clusters are grouped based on their transcriptional similarity to each other. For clusters which express an equal number of marker genes for two different identities both identities were assigned (e.g. cluster 3:Antenna|EAB). Clusters with unresolved identities (i.e. more than two equal assignments) are called “Other”. The colors of the cluster names correspond to the colors in UMAP in (B). The marker score is calculated using a matrix of published marker genes (see Additional file Table S2). (B) UMAP of scRNAseq data. The clusters were annotated based on the heatmap in (A). This UMAP is identical to the UMAP with combined cluster annotation shown in Fig. 3A. Note that the color code is not comparable to the one used in Additional file Figs. S7 and S10. Fig. S5. Fluorescence intensity curves from Bioanalyzer for fresh- and cryopreserved nuclei obtained by different nuclei extraction protocols. (A) The RNA was extracted directly from a fresh sample (36 eye-antennal discs), which was dissociated using the 10x Genomics protocol with 0.1% IGEPAL as a detergent. (B) RNA isolated from a cryopreserved sample, which was dissociated using a protocol based on Triton X-100 as a detergent and a variety of RNAse inhibitors [1]. Note that the sample was thawed for 3.5h before being frozen again. Both curves are close to the expectation of RNA isolated from D. melanogaster [2]. Fig. S6. BioAnalyzer results comparing different nuclei isolation protocols for frozen samples. Samples 1, 2, 4 and 5 were dissociated using the protocol based on Triton X-100 as a detergent and a variety of RNAse inhibitors [1]. Samples 1 and 2 were dissociated by pipetting up and down and samples 4 and 5 were dissociated using a Dounce homogenizer. Samples 7 and 8 were dissociated using the protocol “10x Genomics® Isolation of Nuclei for Single Cell RNA Sequencing” [3]. Samples 7 and 8 were dissociated using only citric acid buffer and samples 10 und 11 were dissociated using only a detergent. Note that for D. melanogaster, intense bands are expected at about 40s and weaker bands at 25s and 45-50s [2]. Each run was repeated once. Fig. S7. Clustering and cluster annotation for snRNAseq data. (A) The heatmap shows the score for each potential cell type (Y-axis) in each cluster (X-axis). The cell types are annotated based on the highest scoring identity in the heatmap. The clusters are grouped based on their transcriptional similarity to each other. For clusters which express an equal number of marker genes for two different identities both identities were assigned (e.g. cluster 15:MFurrow|SMW). Clusters with unresolved identities (i.e. more than two equal assignments) are called “Other”. The colors of the cluster names correspond to the colors in UMAP in (B). The marker score is calculated using a matrix of published marker genes (see Additional file Table S2). (B) UMAP of snRNAseq data. The clusters were annotated based on the heatmap in (A). This UMAP is identical to the UMAP with combined cluster annotation shown in Fig. 3C. Note that the color code is not comparable to the one used in Additional file Figs. S4 and S10. Fig. S8. Gene ontology enrichment analysis for genes with most variable expression. (A) Top 3000 genes unique to scRNAseq (i.e. 1520 genes). (B) Top 3000 genes unique to snRNAseq (i.e. 1520 genes). (C) Top 3000 genes shared between scRNAseq and snRNAseq (i.e. 1480 genes). See also Supplementary Table S9 for a full list of enriched GO terms. Fig. S9. Comparison of dataset specific and shared differentially expressed genes for each cell type. The radar plot shows for each cell type the percentage of cluster specific differentially expressed genes unique for the scRNAseq and snRNAseq data, respectively (red and blue lines), as well as the percentage of differentially expressed genes shared between both datasets (black line). The total number of genes fulfilling the differential expression criteria (FDR 0.05 and log2-fold change > 0.25) for each cell type is shown in brackets. Fig. S10. Clustering and cluster annotation of integrated scRNAseq and snRNAseq dataset. (A) The heatmap shows the score for each potential cell type (Y-axis) in each cluster (X-axis). The cell types are annotated based on the highest scoring identity in the heatmap. The clusters are grouped based on their transcriptional similarity to each other. For clusters which express an equal number of marker genes for two different identities both identities were assigned (e.g. cluster 14:Dorsal|Ocelli). Clusters with unresolved identities (i.e. more than two equal assignments) are called “Other”. The colors of the cluster names correspond to the colors in the UMAP in (B). The marker score is calculated using a matrix of published marker genes (see Additional file Table S2). (B) UMAP of integrated scRNAseq and snRNAseq data. Cells are colored by clusters identified based on the (A). Note that the color code in A and B is not comparable to the one used in Additional file Figs. S4 and S7. Table S1. Overview of different dissociation conditions. Samples within blocks (highlighted in grey and white) were prepared in parallel. The Flow Cytometer only provides percentages of survival because it stops after a defined number of events (i.e. ~50,000 cells) and therefore absolute numbers are not meaningful. “Pipetting” refers to the number of strokes during and after incubation. The cells obtained by experiment/block 12 were subjected to 10X Genomics scRNAseq.

Additional file 1: Figure S1. A kinome-wide CRISPR screen in pancreatic cancer cells. Figure S2. Targeted inhibition of CDK7 enhances gemcitabine and paclitaxel chemotherapy response in pancreatic cancer in vitro and in vivo. Figure S3. CDK7 inhibition induces cell cycle arrest, apoptotic cell death, and DNA damage. Figure S4. Targeted inhibition of CDK7 reverses chemoresistance in pancreatic cancer. Table S1. Cell lines. Table S2. Plasmids and primers list. Table S3. Antibody list. Table S4. List of CRISPR-Cas9 screening results of gemcitabine and paclitaxel in TB32047 cells.

Background. Adolescence hosts a sharp increase in the incidence of mental disorders. The prodromal phases are often characterized by cognitive deficits which predate disease onset by several years. Characterization of cognitive performance in relation to normative trajectories may have value for early risk assessment and monitoring. Methods. Youth aged 8-21 years (n=6481) from the Philadelphia Neurodevelopmental Cohort (PNC) were included. Performance scores from a computerized neurocognitive battery were decomposed using principal component analysis, yielding a general cognitive score. Items reflecting various aspects of psychopathology from self-report questionnaires and collateral caregiver information were decomposed using independent component analysis, providing individual domain scores. Using normative modeling and Bayesian statistics we estimated normative trajectories of cognitive function and tested for associations between cognitive deviance and psychopathological domain scores. In addition, we tested for associations with polygenic scores for mental and behavioral disorders often involving cognition, including schizophrenia, bipolar disorder, attention deficit hyperactivity disorder (ADHD) and Alzheimer’s disease. Results. More negative normative cognitive deviations were associated with higher general psychopathology burden and domains reflecting positive and prodromal psychosis, attention problems, norm-violating behavior, and anxiety. In addition, better performance was associated with higher joint burden of depression, suicidal ideation, and negative psychosis symptoms. The analyses revealed no evidence for associations with polygenic scores. Conclusion. Our results show that cognitive performance is associated with general and specific domains of psychopathology in youth. These findings support the close links between cognition and psychopathology in youth and highlight the potential of normative modeling for early risk assessment.

The odontoblast, with processes beyond the dentin-enamel boundary, is the initial site of TRPC5 cold transduction in teeth., Teeth are composed of many tissues, covered by an inflexible and obdurate enamel. Unlike most other tissues, teeth become extremely cold sensitive when inflamed. The mechanisms of this cold sensation are not understood. Here, we clarify the molecular and cellular components of the dental cold sensing system and show that sensory transduction of cold stimuli in teeth requires odontoblasts. TRPC5 is a cold sensor in healthy teeth and, with TRPA1, is sufficient for cold sensing. The odontoblast appears as the direct site of TRPC5 cold transduction and provides a mechanism for prolonged cold sensing via TRPC5’s relative sensitivity to intracellular calcium and lack of desensitization. Our data provide concrete functional evidence that equipping odontoblasts with the cold-sensor TRPC5 expands traditional odontoblast functions and renders it a previously unknown integral cellular component of the dental cold sensing system.

Background Reptiles are a species-rich group with great phenotypic and life history diversity but are highly underrepresented among the vertebrate species with sequenced genomes. Results Here, we report a high-quality genome assembly of the tegu lizard, Salvator merianae, the first lacertoid with a sequenced genome. We combined 74X Illumina short-read, 29.8X Pacific Biosciences long-read, and optical mapping data to generate a high-quality assembly with a scaffold N50 value of 55.4 Mb. The contig N50 value of this assembly is 521 Kb, making it the most contiguous reptile assembly so far. We show that the tegu assembly has the highest completeness of coding genes and conserved non-exonic elements (CNEs) compared to other reptiles. Furthermore, the tegu assembly has the highest number of evolutionarily conserved CNE pairs, corroborating a high assembly contiguity in intergenic regions. As in other reptiles, long interspersed nuclear elements comprise the most abundant transposon class. We used transcriptomic data, homology- and de novo gene predictions to annotate 22,413 coding genes, of which 16,995 (76%) likely have human orthologs as inferred by CESAR-derived gene mappings. Finally, we generated a multiple genome alignment comprising 10 squamates and 7 other amniote species and identified conserved regions that are under evolutionary constraint. CNEs cover 38 Mb (1.8%) of the tegu genome, with 3.3 Mb in these elements being squamate specific. In contrast to placental mammal-specific CNEs, very few of these squamate-specific CNEs (

Figure S1. Sequencing and assembly strategy of the A. salmonicida subsp. pectinolytica strain 34mel genome. Figure S2. Gene similarity and Mummer alignments to other Aeromonas genomes. Figure S3. Transposon conglomerates from A. salmonicida subsp. pectinolytica strain 34mel. Figure S4. Transposon family content in analyzed Aeromonas strains. Figure S5. Comparison of the A. salmonicida subsp. pectinolytica strain 34mel genome, plasmid pFBAOT6 from Aeromonas caviae, and transposon Tn1721. Table S1. Pathogenic as well as temperature-dependent growth features of analyzed Aeromonas strains. Table S2. General genome features of A. salmonicida subsp. pectinolytica strain 34mel. Table S3. Polymorphic sites in rRNAs from A. salmonicida subsp. pectinolytica strain 34mel. Table S4. Genes disrupted by transposons in A. salmonicida subsp. pectinolytica strain 34mel. Table S5. Transposon details from analyzed Aeromonas strains. Table S6. Genes retained in transposon TnAs1 from the environmental IncP-9 TOL plasmid pWW0 from Pseudomonas putida. Text S1. Annotation details for RNA genes in the A. salmonicida subsp. pectinolytica strain 34mel genome. Text S2. Short unassigned contigs from the A. salmonicida subsp. pectinolytica strain 34mel draft genome. Text S3. Transposons: nomenclature and completeness. Text S4. On transposon counting and the meaning of â ISAs11â . Text S5. Insertions on pFBAOT6 in transposon Tn1721 and details of the regions shared with the A. salmonicida subsp. pectinolytica strain 34mel genome. Text S6. Details of PacBio library preparation, sequencing, and assembly of the genome from A. salmonicida subsp. pectinolytica strain 34mel. (PDF 1171 kb)

Background Clonal competition in cancer describes the process in which the progeny of a cell clone supersedes or succumbs to other competing clones due to differences in their functional characteristics, mostly based on subsequently acquired mutations. Even though the patterns of those mutations are well explored in many tumors, the dynamical process of clonal selection is underexposed. Methods We studied the dynamics of clonal competition in a BcrAbl-induced leukemia using a γ-retroviral vector library encoding the oncogene in conjunction with genetic barcodes. To this end, we studied the growth dynamics of transduced cells on the clonal level both in vitro and in vivo in transplanted mice. Results While we detected moderate changes in clonal abundancies in vitro, we observed monoclonal leukemias in 6/30 mice after transplantation, which intriguingly were caused by only two different BcrAbl clones. To analyze the success of these clones, we applied a mathematical model of hematopoietic tissue maintenance, which indicated that a differential engraftment capacity of these two dominant clones provides a possible explanation of our observations. These findings were further supported by additional transplantation experiments and increased BcrAbl transcript levels in both clones. Conclusion Our findings show that clonal competition is not an absolute process based on mutations, but highly dependent on selection mechanisms in a given environmental context. Electronic supplementary material The online version of this article (doi:10.1186/s12943-017-0668-x) contains supplementary material, which is available to authorized users.

eGFP expression and clonal kinetics in the BcrAbl_bulk culture. Figure S2: Overlap of barcodes from plasmid library and first sample. Figure S3: Clonal kinetics in the eGFP-positive cultures after sorting. Figure S4: Spleens from diseased (and control) animals. Figure S5: Read counts for Klf6 and Sprouty1. Figure S6: Comparison of samples. Figure S7: KEGG enrichment analysis. Table S1: Spleen weights and eGFP expression in the diseased animals. Supplementary Model Description. Table S2: Model Parameters. Table S3: Oligonucleotides. (PDF 7697Â kb)

Den maritime næringen står for omtrent 3% av de globale CO2-utslippene. Med et økende fokus på mer miljøvennlige løsninger og et ønske om å løse dagens klimautfordringer, er hybridelektriske skip essensielle for at den maritime næringen skal klare å redusere utslippene sine. Det å installere batterier på skip er derfor et viktig tiltak for at skipsfarten skal bevege seg i en grønnere retning. Denne avhandlingen presenterer en ny modell for hybridelektriske kraftsystemer, som kalles den strukturbevarende modellen (SPM). SPM-en gjelder opprinnelig kun for konvensjonelle kraftsystemer uten batteri, men et av bidragene i denne avhandlingen er å utvide SPM-en for hybridelektriske kraftsystemer, der en batteri-modell er utviklet. Gjennom en verifikasjonsstudie bekreftes det at SPM-en er en nøyaktig modell for hybridelektriske kraftsystemer, og det konkluderes med at SPM-en er godt egnet til kontrollformål, og at den kan brukes som en modell for å optimalisere energibruken i et hybridelektrisk kraftsystem. Optimaliseringsproblemer som bruker modellprediktiv regulering (MPC) er formulert for tre forskjellige batteristrategier, og løsningene på disse problemene presenteres i en casestudie for et dynamisk posisjonert fartøy. De ulike batteristrategiene som brukes i denne avhandlingen er topputjevning (peak-shaving), kraftutjevning (power smoothing) og strategisk lasting (strategic loading), og i tillegg brukes en strategi uten batteri til sammenligning, som representerer et tradisjonelt kraftsystem. Resultatene fra casestudien indikerer at strategisk lasting er en effektiv batteristrategi. Sammenlignet med å ikke bruke batteri, reduserer strategisk lasting drivstofforbruket med 7% for en kort simulering på 1000 sekunder, og med 5,3% for en lang simulering på 24 timer, som er basert på en realistisk fartøysoperasjon. Selv om det ikke er hovedfokuset i denne avhandlingen, sammenlignes også resultatene med en annen effektiv strategi for å redusere utslipp, såkalt gensettfrakobling, som betyr å slå av og på generatorsett avhengig av lasten. Gensettfrakobling gir store drivstoffbesparelser, og resultatene indikerer at for den korte simuleringen reduseres drivstofforbruket med 16,1% for gensettfrakobling uten batteri, og med 17,7% for gensettfrakobling kombinert med strategisk lasting. For den lange simuleringen er de tilsvarende tallene henholdsvis 38,1% og 38,5%. Følgelig anses det å kombinere gensettfrakobling og strategisk lasting som en effektiv strategi for hybridelektriske skip. Simuleringene viser også at transiente effekter kan oppstå i det hybridelektriske kraftsystemet, enten på grunn av batteriet, som antas å levere effekt øyeblikkelig, eller på grunn av ustabilitet knyttet til gensettfrakobling. Når gensettfrakobling ikke utføres, er frekvensen innenfor de stasjonære grensene satt av klasseselskap, men simuleringene viser at gensettfrakobling kan føre til at disse frekvensgrensene overskrides. The maritime industry accounts for approximately 3% of the global CO2 emissions. With the rising concern for more environmentally friendly solutions and a desire to solve today's climate challenges, hybrid electric ships are essential for the maritime industry to reduce its emissions, and installation of batteries on ships is therefore an important measure for shipping to move in a greener direction. This thesis presents a new model for hybrid electric power systems, which is called the structure-preserving model (SPM). The SPM is originally only valid for conventional power systems without battery, but one of the contributions of this thesis is to extend the SPM for hybrid electric power systems, where a battery model is developed. It is verified that the SPM is an accurate model for hybrid electric power systems through a verification study, and it is concluded that the SPM is well fit for control purposes, and that it can be used as a model to optimize the energy use in a hybrid electric power system. Optimization problems using model predictive control (MPC) are formulated for three different battery strategies, and the solutions to these problems are presented in a case study for a dynamically positioned vessel. The different battery strategies in this thesis are using the battery for peak-shaving, power smoothing and strategic loading, and in addition, a strategy without battery is used for comparison, representing a traditional power system. The results from the case study indicate that strategic loading is an efficient battery strategy. Compared to without using battery, strategic loading reduces the fuel consumption with 7% for a short simulation of 1000 seconds, and with 5.3% for a long simulation of 24 hours, based on a realistic vessel operation. Even though not the main focus of this thesis, the results are also compared to another efficient strategy for reducing emissions, which is genset disconnection, meaning turning on and off generator sets depending on the load. Genset disconnection gives large fuel savings, and the results indicate that for the short simulation, the fuel consumption is reduced by 16.1% for genset disconnection without battery, and by 17.7% for genset disconnection combined with strategic loading. For the long simulation, the corresponding numbers are 38.1% and 38.5%, respectively. Consequently, combining genset disconnection and strategic loading is deemed an efficient strategy for hybrid electric ships. The simulations also show that transient effects can occur in the hybrid electric power system, either due to the battery power, which is assumed to be delivered instantly, or due to instabilities from the genset disconnection. When not performing genset disconnection, the frequency is within the steady-state limits set by class societies, but the simulations show that genset disconnection can make the frequency exceed these limits.