Genome-wide quantification of copy-number aberration impact on gene expression in ovarian high-grade serous carcinoma.

Copy-number alterations (CNAs) are a hallmark of cancer and can regulate cancer cell states via altered gene expression values. Herein, we have developed a copy-number impact (CNI) analysis method that quantifies the degree to which a gene expression value is impacted by CNAs and leveraged this analysis at the pathway level. Our results show that a high CNA is not necessarily reflected at the gene expression level, and our method is capable of detecting genes and pathways whose activity is strongly influenced by CNAs. Furthermore, the CNI analysis enables unbiased categorization of CNA categories, such as deletions and amplifications. We identified six CNI-driven pathways associated with poor treatment response in ovarian high-grade serous carcinoma (HGSC), which we found to be the most CNA-driven cancer across 14 cancer types. The key driver in most of these pathways was amplified wild-type KRAS, which we validated functionally using CRISPR modulation. Our results suggest that wild-type KRAS amplification is a driver of chemotherapy resistance in HGSC and may serve as a potential treatment target.

Simultaneous adsorption of Cu(II), Zn(II), Cd(II) and Pb(II) from synthetic wastewater using NaP and LTA zeolites prepared from biomass fly ash.

Herein, NaP and LTA zeolites were successfully synthesised from woody biomass ash with alkali fusion-assisted hydrothermal method by altering the NaOH/ash ratio, crystallisation time and crystallisation temperature. In order to reduce the synthesis costs, NaP zeolite was synthesised with no additional source of aluminium and silicon. The synthesised zeolites were utilized for the monocomponent and simultaneous adsorption of Cu(II), Cd(II), Pb(II) and Zn(II) ions. The maximum adsorbed amount of metals had the trend Pb(II) > Cu(II) > Cd(II) > Zn(II) for both NaP and LTA zeolite. The kinetic data fit well to the pseudo-second order model indicating that chemisorption is the rate-limiting step. The isotherm data were well described with Sips and Redlich-Peterson models indicating a non-ideal heterogeneous adsorption process. Maximum adsorption capacity of NaP zeolite was 42.9 mg/g for Cu(II) and 117.3 mg/g for Cd(II), while LTA had 140.1 mg/g and 223.5 mg/g for Cu(II) and Cd(II) ions, respectively.

Evolutionary states and trajectories characterized by distinct pathways stratify patients with ovarian high grade serous carcinoma.

Ovarian high-grade serous carcinoma (HGSC) is typically diagnosed at an advanced stage, with multiple genetically heterogeneous clones existing in the tumors long before therapeutic intervention. Herein we integrate clonal composition and topology using whole-genome sequencing data from 510 samples of 148 patients with HGSC in the prospective, longitudinal, multiregion DECIDER study. Our results reveal three evolutionary states, which have distinct features in genomics, pathways, and morphological phenotypes, and significant association with treatment response. Nested pathway analysis suggests two evolutionary trajectories between the states. Experiments with five tumor organoids and three PI3K inhibitors support targeting tumors with enriched PI3K/AKT pathway with alpelisib. Heterogeneity analysis of samples from multiple anatomical sites shows that site-of-origin samples have 70% more unique clones than metastatic tumors or ascites. In conclusion, these analysis and visualization methods enable integrative tumor evolution analysis to identify patient subtypes using data from longitudinal, multiregion cohorts.

The transcription factor network of E. coli steers global responses to shifts in RNAP concentration.

The robustness and sensitivity of gene networks to environmental changes is critical for cell survival. How gene networks produce specific, chronologically ordered responses to genome-wide perturbations, while robustly maintaining homeostasis, remains an open question. We analysed if short- and mid-term genome-wide responses to shifts in RNA polymerase (RNAP) concentration are influenced by the known topology and logic of the transcription factor network (TFN) of Escherichia coli. We found that, at the gene cohort level, the magnitude of the single-gene, mid-term transcriptional responses to changes in RNAP concentration can be explained by the absolute difference between the gene's numbers of activating and repressing input transcription factors (TFs). Interestingly, this difference is strongly positively correlated with the number of input TFs of the gene. Meanwhile, short-term responses showed only weak influence from the TFN. Our results suggest that the global topological traits of the TFN of E. coli shape which gene cohorts respond to genome-wide stresses.

POIBM: batch correction of heterogeneous RNA-seq datasets through latent sample matching.

MOTIVATION: RNA sequencing and other high-throughput technologies are essential in understanding complex diseases, such as cancers, but are susceptible to technical factors manifesting as patterns in the measurements. These batch patterns hinder the discovery of biologically relevant patterns. Unbiased batch effect correction in heterogeneous populations currently requires special experimental designs or phenotypic labels, which are not readily available for patient samples in existing datasets. RESULTS: We present POIBM, an RNA-seq batch correction method, which learns virtual reference samples directly from the data. We use a breast cancer cell line dataset to show that POIBM exceeds or matches the performance of previous methods, while being blind to the phenotypes. Further, we analyze The Cancer Genome Atlas RNA-seq data to show that batch effects plague many cancer types; POIBM effectively discovers the true replicates in stomach adenocarcinoma; and integrating the corrected data in endometrial carcinoma improves cancer subtyping. AVAILABILITY AND IMPLEMENTATION: https://bitbucket.org/anthakki/poibm/ (archived at https://doi.org/10.5281/zenodo.6122436). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

QuantISH: RNA in situ hybridization image analysis framework for quantifying cell type-specific target RNA expression and variability.

RNA in situ hybridization (RNA-ISH) is a powerful spatial transcriptomics technology to characterize target RNA abundance and localization in individual cells. This allows analysis of tumor heterogeneity and expression localization, which are not readily obtainable through transcriptomic data analysis. RNA-ISH experiments produce large amounts of data and there is a need for automated analysis methods. Here we present QuantISH, a comprehensive open-source RNA-ISH image analysis pipeline that quantifies marker expressions in individual carcinoma, immune, and stromal cells on chromogenic or fluorescent in situ hybridization images. QuantISH is designed to be modular and can be adapted to various image and sample types and staining protocols. We show that in chromogenic RNA in situ hybridization images of high-grade serous carcinoma (HGSC) QuantISH cancer cell classification has high precision, and signal expression quantification is in line with visual assessment. We further demonstrate the power of QuantISH by showing that CCNE1 average expression and DDIT3 expression variability, as captured by the variability factor developed herein, act as candidate biomarkers in HGSC. Altogether, our results demonstrate that QuantISH can quantify RNA expression levels and their variability in carcinoma cells, and thus paves the way to utilize RNA-ISH technology.

Longitudinal single-cell RNA-seq analysis reveals stress-promoted chemoresistance in metastatic ovarian cancer.

Chemotherapy resistance is a critical contributor to cancer mortality and thus an urgent unmet challenge in oncology. To characterize chemotherapy resistance processes in high-grade serous ovarian cancer, we prospectively collected tissue samples before and after chemotherapy and analyzed their transcriptomic profiles at a single-cell resolution. After removing patient-specific signals by a novel analysis approach, PRIMUS, we found a consistent increase in stress-associated cell state during chemotherapy, which was validated by RNA in situ hybridization and bulk RNA sequencing. The stress-associated state exists before chemotherapy, is subclonally enriched during the treatment, and associates with poor progression-free survival. Co-occurrence with an inflammatory cancer-associated fibroblast subtype in tumors implies that chemotherapy is associated with stress response in both cancer cells and stroma, driving a paracrine feed-forward loop. In summary, we have found a resistant state that integrates stromal signaling and subclonal evolution and offers targets to overcome chemotherapy resistance.

Cardiovascular disease in people born to unmarried mothers in two historical periods: The Helsinki Birth Cohort Study 1934-1944.

Aims:Socio-economic conditions in early life are important contributors to cardiovascular disease - the leading cause of mortality globally - in later life. We studied coronary heart disease (CHD) and stroke in adulthood among people born out of wedlock in two historical periods: before and during World War II in Finland. Methods: We compared offspring born out of wedlock before (1934-1939) and during (1940-1944) World War II with the offspring of married mothers in the Helsinki Birth Cohort Study. The war affected the position of unmarried mothers in society. We followed the study subjects from 1971 to 2014 and identified deaths and hospital admissions from CHD and stroke. Data were analysed using a Cox regression, adjusting for other childhood and adulthood socio-economic circumstances. Results: The rate of out-of-wedlock births was 240/4052 (5.9%) before World War II and 397/9197 (4.3%) during World War II. Among those born before World War II, out-of-wedlock birth was associated with an increased risk of stroke (hazard ratio (HR)=1.44; 95% confidence interval (CI) 1.00-2.07) and CHD (HR=1.37; 95% CI 1.02-1.86). Among those born out of wedlock during World War II, the risks of stroke (HR=0.89; 95% CI 0.58-1.36) and CHD (HR=0.70; 95% CI 0.48=1.03) were similar to those observed for the offspring of married mothers. The p-values for interaction of unmarried×World War II were (p=0.015) for stroke and (p=0.003) for CHD. Conclusions: In a society in which marriage is normative, being born out of wedlock is an important predictor of lifelong health disadvantage. However, this may change rapidly when societal circumstances change, such as during a war.

Network-guided identification of cancer-selective combinatorial therapies in ovarian cancer.

Each patient's cancer consists of multiple cell subpopulations that are inherently heterogeneous and may develop differing phenotypes such as drug sensitivity or resistance. A personalized treatment regimen should therefore target multiple oncoproteins in the cancer cell populations that are driving the treatment resistance or disease progression in a given patient to provide maximal therapeutic effect, while avoiding severe co-inhibition of non-malignant cells that would lead to toxic side effects. To address the intra- and inter-tumoral heterogeneity when designing combinatorial treatment regimens for cancer patients, we have implemented a machine learning-based platform to guide identification of safe and effective combinatorial treatments that selectively inhibit cancer-related dysfunctions or resistance mechanisms in individual patients. In this case study, we show how the platform enables prediction of cancer-selective drug combinations for patients with high-grade serous ovarian cancer using single-cell imaging cytometry drug response assay, combined with genome-wide transcriptomic and genetic profiles. The platform makes use of drug-target interaction networks to prioritize those combinations that warrant further preclinical testing in scarce patient-derived primary cells. During the case study in ovarian cancer patients, we investigated (i) the relative performance of various ensemble learning algorithms for drug response prediction, (ii) the use of matched single-cell RNA-sequencing data to deconvolute cell population-specific transcriptome profiles from bulk RNA-seq data, (iii) and whether multi-patient or patient-specific predictive models lead to better predictive accuracy. The general platform and the comparison results are expected to become useful for future studies that use similar predictive approaches also in other cancer types.

PRISM: recovering cell-type-specific expression profiles from individual composite RNA-seq samples.

MOTIVATION: A major challenge in analyzing cancer patient transcriptomes is that the tumors are inherently heterogeneous and evolving. We analyzed 214 bulk RNA samples of a longitudinal, prospective ovarian cancer cohort and found that the sample composition changes systematically due to chemotherapy and between the anatomical sites, preventing direct comparison of treatment-naive and treated samples. RESULTS: To overcome this, we developed PRISM, a latent statistical framework to simultaneously extract the sample composition and cell-type-specific whole-transcriptome profiles adapted to each individual sample. Our results indicate that the PRISM-derived composition-free transcriptomic profiles and signatures derived from them predict the patient response better than the composite raw bulk data. We validated our findings in independent ovarian cancer and melanoma cohorts, and verified that PRISM accurately estimates the composition and cell-type-specific expression through whole-genome sequencing and RNA in situ hybridization experiments. AVAILABILITYAND IMPLEMENTATION: https://bitbucket.org/anthakki/prism. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Differential DNA methylation in recovery from shift work disorder.

The human DNA methylome is responsive to our environment, but its dynamics remain underexplored. We investigated the temporal changes to DNA methylation (DNAme) in relation to recovery from a shift work disorder (SWD) by performing a paired epigenome-wide analysis in an occupational cohort of 32 shift workers (25 men, age = 43.8 ± 8.8 years, 21 SWD cases). We found that the effect of vacation on DNAme was more prominent in the SWD-group as compared to controls, with respect to the amount of significantly differentially methylated positions (DMPs; Punadj < 0.05) 6.5 vs 3.7%, respectively. The vast majority (78%) of these DMPs were hypomethylated in SWD but not in controls (27%) during the work period. The Gene Ontology Cellular component "NMDA glutamate receptor" (PFDR < 0.05) was identified in a pathway analysis of the top 30 genes in SWD. In-depth pathway analyses revealed that the Reactome pathway "CREB phosphorylation through the activation of CaMKII" might underlie the recovery. Furthermore, three DMPs from this pathway, corresponding to GRIN2C, CREB1, and CAMK2B, correlated with the degree of recovery (Punadj < 0.05). Our findings provide evidence for the dynamic nature of DNAme in relation to the recovery process from a circadian disorder, with biological relevance of the emerging pathways.

Agile workflow for interactive analysis of mass cytometry data.

MOTIVATION: Single-cell proteomics technologies, such as mass cytometry, have enabled characterization of cell-to-cell variation and cell populations at a single-cell resolution. These large amounts of data, require dedicated, interactive tools for translating the data into knowledge. RESULTS: We present a comprehensive, interactive method called Cyto to streamline analysis of large-scale cytometry data. Cyto is a workflow-based open-source solution that automates the use of state-of-the-art single-cell analysis methods with interactive visualization. We show the utility of Cyto by applying it to mass cytometry data from peripheral blood and high-grade serous ovarian cancer (HGSOC) samples. Our results show that Cyto is able to reliably capture the immune cell sub-populations from peripheral blood and cellular compositions of unique immune- and cancer cell subpopulations in HGSOC tumor and ascites samples. AVAILABILITYAND IMPLEMENTATION: The method is available as a Docker container at https://hub.docker.com/r/anduril/cyto and the user guide and source code are available at https://bitbucket.org/anduril-dev/cyto. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

qSNE: quadratic rate t-SNE optimizer with automatic parameter tuning for large datasets.

MOTIVATION: Non-parametric dimensionality reduction techniques, such as t-distributed stochastic neighbor embedding (t-SNE), are the most frequently used methods in the exploratory analysis of single-cell datasets. Current implementations scale poorly to massive datasets and often require downsampling or interpolative approximations, which can leave less-frequent populations undiscovered and much information unexploited. RESULTS: We implemented a fast t-SNE package, qSNE, which uses a quasi-Newton optimizer, allowing quadratic convergence rate and automatic perplexity (level of detail) optimizer. Our results show that these improvements make qSNE significantly faster than regular t-SNE packages and enables full analysis of large datasets, such as mass cytometry data, without downsampling. AVAILABILITY AND IMPLEMENTATION: Source code and documentation are openly available at https://bitbucket.org/anthakki/qsne/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Pressure filtration properties of sludge generated in the electrochemical treatment of mining waters.

In this study, batch electrocoagulation (EC) experiments were performed with synthetic mining water in various conditions in a laboratory-scale 1L reactor. The process was scaled up and the selected results were verified with both synthetic and real mining water in a 70 L reactor. The generated solids were characterized by XRD, SEM, and a laser diffraction particle size analyzer. After preconcentration by settling and decantation, the EC solids were separated by constant pressure filtration at 2-6 bar. In order to improve the separation, various filter aids were used in body-feed and precoat modes. The results show that the overall removal efficiency was the highest with consumable electrode pairs such as Fe/Fe, Al/Al and Fe/Al, and the highest treatment efficiency was achieved with Fe/Al electrodes where 100/100% of the nitrate and 96/87% of the sulfate were removed in small/large-scale experiments. Depending on the dissolved electrode material, different solid species were formed: crystalline primary particles with a minor degree of agglomeration were observed in Fe/Fe slurry, whereas aluminium-containing solids (Al/Al and Fe/Al) were mainly amorphous agglomerates. High values of average specific cake resistances (αav = 2·1012 - 4·1013), average porosities (>90%) and moisture contents (>68 wt%) of filter cakes were obtained for all filtered samples. The highest values of the above-mentioned cake characteristics were observed for aluminium-based solids, which might be explained by its highly amorphous structure. The application of filter aids improved the filterability of the sludges by reducing the average specific cake resistance by as much as 95-96% in the body-feed mode and by 84% in the precoat mode.

Transcription closed and open complex formation coordinate expression of genes with a shared promoter region.

Many genes are spaced closely, allowing coordination without explicit control through shared regulatory elements and molecular interactions. We study the dynamics of a stochastic model of a gene-pair in a head-to-head configuration, sharing promoter elements, which accounts for the rate-limiting steps in transcription initiation. We find that only in specific regions of the parameter space of the rate-limiting steps is orderly coexpression exhibited, suggesting that successful cooperation between closely spaced genes requires the coevolution of compatible rate-limiting step configuration. The model predictions are validated using in vivo single-cell, single-RNA measurements of the dynamics of pairs of genes sharing promoter elements. Our results suggest that, in E. coli, the kinetics of the rate-limiting steps in active transcription can play a central role in shaping the dynamics of gene-pairs sharing promoter elements.

Anduril 2: upgraded large-scale data integration framework.

SUMMARY: Anduril is an analysis and integration framework that facilitates the design, use, parallelization and reproducibility of bioinformatics workflows. Anduril has been upgraded to use Scala for pipeline construction, which simplifies software maintenance, and facilitates design of complex pipelines. Additionally, Anduril's bioinformatics repository has been expanded with multiple components, and tutorial pipelines, for next-generation sequencing data analysis. AVAILABILITYAND IMPLEMENTATION: Freely available at http://anduril.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identifying differentially methylated sites in samples with varying tumor purity.

Motivation: DNA methylation aberrations are common in many cancer types. A major challenge hindering comparison of patient-derived samples is that they comprise of heterogeneous collection of cancer and microenvironment cells. We present a computational method that allows comparing cancer methylomes in two or more heterogeneous tumor samples featuring differing, unknown fraction of cancer cells. The method is unique in that it allows comparison also in the absence of normal cell control samples and without prior tumor purity estimates, as these are often unavailable or unreliable in clinical samples. Results: We use simulations and next-generation methylome, RNA and whole-genome sequencing data from two cancer types to demonstrate that the method is accurate and outperforms alternatives. The results show that our method adapts well to various cancer types and to a wide range of tumor content, and works robustly without a control or with controls derived from various sources. Availability and implementation: The method is freely available at https://bitbucket.org/anthakki/dmml. Supplementary information: Supplementary data are available at Bioinformatics online.

SCIP: a single-cell image processor toolbox.

Summary: Each cell is a phenotypically unique individual that is influenced by internal and external processes, operating in parallel. To characterize the dynamics of cellular processes one needs to observe many individual cells from multiple points of view and over time, so as to identify commonalities and variability. With this aim, we engineered a software, 'SCIP', to analyze multi-modal, multi-process, time-lapse microscopy morphological and functional images. SCIP is capable of automatic and/or manually corrected segmentation of cells and lineages, automatic alignment of different microscopy channels, as well as detect, count and characterize fluorescent spots (such as RNA tagged by MS2-GFP), nucleoids, Z rings, Min system, inclusion bodies, undefined structures, etc. The results can be exported into *mat files and all results can be jointly analyzed, to allow studying not only each feature and process individually, but also find potential relationships. While we exemplify its use on Escherichia coli, many of its functionalities are expected to be of use in analyzing other prokaryotes and eukaryotic cells as well. We expect SCIP to facilitate the finding of relationships between cellular processes, from small-scale (e.g. gene expression) to large-scale (e.g. cell division), in single cells and cell lineages. Availability and implementation: http://www.ca3-uninova.org/project_scip. Supplementary information: Supplementary data are available at Bioinformatics online.

Extraction of hazardous metals from green liquor dregs by ethylenediaminetetraacetic acid.

Green liquor dregs are the major inorganic solid side stream of kraft pulp mills which contain environmentally hazardous metals. The presence of hazardous metals in this industrial residue brings statutory limits for its landfilling, although they are not easily mobilized from the solid phase. In this study, the chelating agent ethylenediaminetetraacetic acid (EDTA) is utilized to extract hazardous metals such as Cd, Pb and Zn from green liquor dregs. Furthermore, the influence of EDTA on the removal of Ca as the main mineral nutrient present in the green liquor dregs is studied. The effect of parameters such as EDTA dosage, L/S ratio and contact time on the removal rate of the elements is investigated. In addition, the experimental data are fitted to the Elovich model and the pseudo-first-order model to describe the desorption kinetics. The results show that 59 wt% of Cd, 13 wt% of Co, 62 wt% of Cu, 3 wt% of Mn, 12 wt% of Ni, 43 wt% of Pb, 16 wt% of Zn, and less than 1 wt% of Ca were extracted from green liquor dregs with EDTA dosage of 0.035 gEDTA salt/gdregs and the L/S ratio of 6.25 ml/g. The current study opens up new possibilities to use the green liquor dregs for improving the soil fertility instead of landfilling.

Effective removal of hazardous trace metals from recovery boiler fly ashes.

The objective of this study is to introduce a treatment sequence enabling straightforward and effective recovery of hazardous trace elements from recovery boiler fly ash (RBFA) by a novel method, and to demonstrate the subsequent removal of Cl and K with the existing crystallization technology. The treatment sequence comprises two stages: dissolution of most other RBFA components than the hazardous trace elements in water in Step 1 of the treatment, and crystallization of the process chemicals in Step 2. Solid-liquid separation has an important role in the treatment, due to the need to separate first the small solid residue containing the trace elements, and to separate the valuable crystals, containing Na and S, from the liquid rich in Cl and K. According to the results, nearly complete recovery of cadmium, lead and zinc can be reached even without pH adjustment. Some other metals, such as Mg and Mn, are removed together with the hazardous metals. Regarding the removal of Cl and K from the process, in this non-optimized case the removal efficiency was satisfactory: 60-70% for K when 80% of sodium was recovered, and close to 70% for Cl when 80% of sulfate was recovered.