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Background and purpose: Periprosthetic joint infection (PJI) is the commonest reason for revision after total knee arthroplasty (TKA). We assessed the risk factors for revision due to PJI following TKA based on the Finnish Arthroplasty Register (FAR). Patients and methods: We analyzed 62,087 primary condylar TKAs registered between June 2014 and February 2020 with revision for PJI as the endpoint. Cox proportional hazards regression was used to estimate hazard ratios (HR) with 95% confidence intervals (CI) for the first PJI revision using 25 potential patient- and surgical-related risk factors as covariates.Results: 484 knees were revised for the first time during the first postoperative year because of PJI. The HRs for revision due to PJI in unadjusted analysis were 0.5 (0.4–0.6) for female sex, 0.7 (0.6–1.0) for BMI 25–29, and 1.6 (1.1–2.5) for BMI > 40 compared with BMI < 25, 4.0 (1.3–12) for preoperative fracture diagnosis compared with osteoarthritis, and 0.7 (0.5–0.9) for use of an antimicrobial incise drape. In adjusted analysis the HRs were 2.2 (1.4–3.5) for ASA class III–IV compared with class I, 1.7 (1.4–2.1) for intraoperative bleeding ≥ 100 mL, 1.4 (1.2–1.8) for use of a drain, 0.7 (0.5–1.0) for short duration of operation of 45–59 minutes, and 1.7 (1.3–2.3) for long operation duration > 120 min compared with 60–89 minutes, and 1.3 (1.0–1.8) for use of general anesthesia.Conclusion: We found increased risk for revision due to PJI when no incise drape was used. The use of drainage also increased the risk. Specializing in performing TKA reduces operative time and thereby also the PJI rate.

Supplementary tables S1-S5 and supplementary figures S1-S5

Basal-like breast cancers (BLBC) are characterized by defects in homologous recombination (HR), deficient mitotic checkpoint, and high-proliferation activity. Here, we discover CIP2A as a candidate driver of BLBC. CIP2A was essential for DNA damage–induced initiation of mouse BLBC-like mammary tumors and for survival of HR–defective BLBC cells. CIP2A was dispensable for normal mammary gland development and for unperturbed mitosis, but selectively essential for mitotic progression of DNA damaged cells. A direct interaction between CIP2A and a DNA repair scaffold protein TopBP1 was identified, and CIP2A inhibition resulted in enhanced DNA damage–induced TopBP1 and RAD51 recruitment to chromatin in mammary epithelial cells. In addition to its role in tumor initiation, and survival of BRCA-deficient cells, CIP2A also drove proliferative MYC and E2F1 signaling in basal-like triple-negative breast cancer (BL-TNBC) cells. Clinically, high CIP2A expression was associated with poor patient prognosis in BL-TNBCs but not in other breast cancer subtypes. Small-molecule reactivators of PP2A (SMAP) inhibited CIP2A transcription, phenocopied the CIP2A-deficient DNA damage response (DDR), and inhibited growth of patient-derived BLBC xenograft. In summary, these results demonstrate that CIP2A directly interacts with TopBP1 and coordinates DNA damage–induced mitotic checkpoint and proliferation, thereby driving BLBC initiation and progression. SMAPs could serve as a surrogate therapeutic strategy to inhibit the oncogenic activity of CIP2A in BLBCs.Significance:These results identify CIP2A as a nongenetic driver and therapeutic target in basal-like breast cancer that regulates DNA damage–induced G2–M checkpoint and proliferative signaling.

Supplementary Data from CIP2A Interacts with TopBP1 and Drives Basal-Like Breast Cancer Tumorigenesis

Purpose:Cancerous inhibitor of protein phosphatase 2A (CIP2A) is an oncoprotein that inhibits the tumor suppressor PP2A-B56α. However, CIP2A mRNA variants remain uncharacterized. Here, we report the discovery of a CIP2A splicing variant, novel CIP2A variant (NOCIVA).Experimental Design:Characterization of CIP2A variants was performed by both 3′ and 5′ rapid amplification of cDNA ends from cancer cells. The function of NOCIVA was assessed by structural and molecular biology approaches. Its clinical relevance was studied in an acute myeloid leukemia (AML) patient cohort and two independent chronic myeloid leukemia (CML) cohorts.Results:NOCIVA contains CIP2A exons 1 to 13 fused to 349 nucleotides from CIP2A intron 13. Intriguingly, the first 39 nucleotides of the NOCIVA-specific sequence are in the coding frame with exon 13 of CIP2A and code for a 13-amino acid peptide tail nonhomologous to any known human protein sequence. Therefore, NOCIVA translates to a unique human protein. NOCIVA retains the capacity to bind to B56α, but, whereas CIP2A is predominantly a cytoplasmic protein, NOCIVA translocates to the nucleus. Indicative of prevalent alternative splicing from CIP2A to NOCIVA in myeloid malignancies, AML and CML patient samples overexpress NOCIVA, but not CIP2A mRNA. In AML, a high NOCIVA/CIP2A mRNA expression ratio is a marker for adverse overall survival. In CML, high NOCIVA expression is associated with inferior event-free survival among imatinib-treated patients, but not among patients treated with dasatinib or nilotinib.Conclusions:We discovered a novel variant of the oncoprotein CIP2A and its clinical relevance in predicting tyrosine kinase inhibitor therapy resistance in myeloid leukemias.

BackgroundHeterogeneity in the rate of β-cell loss in newly diagnosed type 1 diabetes patients is poorly understood and creates a barrier to designing and interpreting disease-modifying clinical trials. Integrative analyses of complementary multi-omics data obtained after the diagnosis of T1D may provide mechanistic insight into the diverse rates of disease progression.MethodsWe collected samples in a pan-European consortium that enabled the concerted analysis of five different omics modalities in data from 97 newly diagnosed patients. In this study we used Multi-Omics Factor Analysis to identify molecular signatures correlating with post-diagnosis decline in β-cell mass measured as fasting C-peptide.ResultsTwo molecular signatures were significantly correlated with fasting C-peptide levels. One signature showed a correlation to neutrophil degranulation, cytokine signaling, lymphoid and non-lymphoid cell interactions and G-protein coupled receptor signaling events that were inversely associated with rapid decline in β-cell function. The second signature was related to translation and viral infection were inversely associated with change in β-cell function. In addition, the immunomics data revealed a Natural Killer cell signature associated with rapid β-cell decline.ConclusionFeatures that differ between individuals with slow and rapid decline in β-cell mass could be valuable in staging and prediction of the rate of disease progression and thus enable smarter (shorter and smaller) trial designs for disease modifying therapies, as well as offering biomarkers of therapeutic effect.FundingThis work is funded by the Innovative Medicine Initiative 2 Joint Undertaking (IMI2 JU) under grant agreement N° 115797 (INNODIA) and N° 945268 (INNODIA HARVEST). This Joint Undertaking receives support from the Union’s Horizon 2020 research and innovation program and ‘EFPIA’, ‘JDRF’ and ‘The Leona M. and Harry B. Helmsley Charitable Trust’.

The increasing amount of transcriptomic data has brought to light vast numbers of potential novel RNA transcripts. Accurately distinguishing novel long non-coding RNAs (lncRNAs) from protein-coding messenger RNAs (mRNAs) has challenged bioinformatic tool developers. Most recently, tools implementing deep learning architectures have been developed for this task, with the potential of discovering sequence features and their interactions still not surfaced in current knowledge. We compared the performance of deep learning tools with other predictive tools that are currently used in lncRNA coding potential prediction. A total of 15 tools representing the variety of available methods were investigated. In addition to known annotated transcripts, we also evaluated the use of the tools in actual studies with real-life data. The robustness and scalability of the tools’ performance was tested with varying sized test sets and test sets with different proportions of lncRNAs and mRNAs. In addition, the ease-of-use for each tested tool was scored. Deep learning tools were top performers in most metrics and labelled transcripts similarly with each other in the real-life dataset. However, the proportion of lncRNAs and mRNAs in the test sets affected the performance of all tools. Computational resources were utilized differently between the top-ranking tools, thus the nature of the study may affect the decision of choosing one well-performing tool over another. Nonetheless, the results suggest favouring the novel deep learning tools over other tools currently in broad use.

Despite the relatively high frequency of somatic ERBB4 mutations in various cancer types, only a few activating ERBB4 mutations have been characterized, primarily due to lack of mutational hotspots in the ERBB4 gene. Here, we utilized our previously published pipeline, an in vitro screen for activating mutations, to perform an unbiased functional screen to identify potential activating ERBB4 mutations from a randomly mutated ERBB4 expression library. Ten potentially activating ERBB4 mutations were identified and subjected to validation by functional and structural analyses. Two of the 10 ERBB4 mutants, E715K and R687K, demonstrated hyperactivity in all tested cell models and promoted cellular growth under two-dimensional and three-dimensional culture conditions. ERBB4 E715K also promoted tumor growth in in vivo Ba/F3 cell mouse allografts. Importantly, all tested ERBB4 mutants were sensitive to the pan-ERBB tyrosine kinase inhibitors afatinib, neratinib, and dacomitinib. Our data indicate that rare ERBB4 mutations are potential candidates for ERBB4-targeted therapy with pan-ERBB inhibitors. Statement of Significance: ERBB4 is a member of the ERBB family of oncogenes that is frequently mutated in different cancer types but the functional impact of its somatic mutations remains unknown. Here, we have analyzed the function of over 8,000 randomly mutated ERBB4 variants in an unbiased functional genetics screen. The data indicate the presence of rare activating ERBB4 mutations in cancer, with potential to be targeted with clinically approved pan-ERBB inhibitors.

Background Various indexes have been developed to estimate the risk for mortality, institutionalization, and other adverse outcomes for older people. Most indexes are based on a large number of clinical or laboratory parameters. An index based on only a few parameters would be more practical to use in every-day clinical practice. Our aim was to create an index to predict the risk for mortality and institutionalization with as few parameters as possible without compromising their predictive ability. Methods A prospective study with a 10-year follow-up period. Thirty-six clinical and fourteen laboratory parameters were combined to form an index. Cox regression model was used to analyze the association of the index with institutionalization and mortality. A backward statistical method was used to reduce the number of parameters to form an easy-to-use index for predicting institutionalization and mortality. Results The mean age of the participants (n = 1172) was 73.1 (SD 6.6, range 64‒97) years. Altogether, 149 (14%) subjects were institutionalized, and 413 (35%) subjects deceased during the follow-up. Institutionalization and mortality rates increased as index scores increased both for the large 50-parameter combined index and for the reduced indexes. After a backward variable selection in the Cox regression model, three clinical parameters remained in the index to predict institutionalization and six clinical and three laboratory parameters in the index to predict mortality. The reduced indexes showed a slightly better predictive value for both institutionalization and mortality compared to the full index. Conclusions A large index with fifty parameters included many unimportant parameters that did not increase its predictive value, and therefore could be replaced with a reduced index with only a few carefully chosen parameters, that were individually associated with institutionalization or death.

Quantitative proteomics has matured into an established tool and longitudinal proteomics experiments have begun to emerge. However, no effective, simple-to-use differential expression method for longitudinal proteomics data has been released. Typically, such data is noisy, contains missing values, and has only few time points and biological replicates. To address this need, we provide a comprehensive evaluation of several existing differential expression methods for high-throughput longitudinal omics data and introduce a Robust longitudinal Differential Expression (RolDE) approach. The methods are evaluated using over 3000 semi-simulated spike-in proteomics datasets and three large experimental datasets. In the comparisons, RolDE performs overall best; it is most tolerant to missing values, displays good reproducibility and is the top method in ranking the results in a biologically meaningful way. Furthermore, RolDE is suitable for different types of data with typically unknown patterns in longitudinal expression and can be applied by non-experienced users.

Background and purpose — Periprosthetic joint infection (PJI) is a devastating complication and more information on risk factors for PJI is required to find measures to prevent infections. Therefore, we assessed risk factors for PJI after primary total hip arthroplasty (THA) in a large patient cohort. Patients and methods — We analyzed 33,337 primary THAs performed between May 2014 and January 2018 based on the Finnish Arthroplasty Register (FAR). Cox proportional hazards regression was used to estimate hazard ratios with 95% confidence intervals (CI) for first PJI revision operation using 25 potential patient- and surgical-related risk factors as covariates. Results — 350 primary THAs were revised for the first time due to PJI during the study period. The hazard ratios for PJI revision in multivariable analysis were 2.0 (CI 1.3–3.2) for ASA class II and 3.2 (2.0–5.1) for ASA class III–IV compared with ASA class I, 1.4 (1.1–1.7) for bleeding > 500 mL compared with < 500 mL, 0.4 (0.2–0.7) for ceramic-on-ceramic bearing couple compared with metal-on-polyethylene and for the first 3 postoperative weeks, 3.0 (1.6–5.6) for operation time of > 120 minutes compared with 45–59 minutes, and 2.6 (1.4–4.9) for simultaneous bilateral operation. In the univariable analysis, hazard ratios for PJI revision were 2.3 (1.7–3.3) for BMI of 31–35 and 5.0 (3.5–7.1) for BMI of > 35 compared with patients with BMI of 21–25. Interpretation — We found several modifiable risk factors associated with increased PJI revision risk after THA to which special attention should be paid preoperatively. In particular, high BMI may be an even more prominent risk factor for PJI than previously assessed. publishedVersion

The sequence contexts of genomic variants play important roles in understanding biological significances of variants and potential sequencing related variant calling issues. However, methods for assessing the diverse sequence contexts of genomic variants such as tandem repeats and unambiguous annotations have been limited. Herein, we describe the Variant Sequence Context Annotation Tool (VarSCAT) for annotating the sequence contexts of genomic variants, including breakpoint ambiguities, flanking sequences, variant nomenclatures, adjacent variants, and tandem repeats with user customizable options. Our analysis demonstrate that VarSCAT is more versatile and customizable than current methods or strategies for annotating variants in short tandem repeat (STR) regions. Variant sequence context annotations of high-confidence human variant sets with VarSCAT revealed that more than 75% of all human individual germline and clinically relevant insertions and deletions (indels) have breakpoint ambiguities. Moreover, we illustrate that more than 80% of human individual germline small variants in STR regions are indels and that the sizes of these indels correlated with STR motif sizes. VarSCAT is available athttps://github.com/elolab/VarSCAT.Author SummaryThe sequence contexts have significant impacts on the biological and technical aspects of genomic variants. The sequence contexts, such as tandem repeats or nearby indels, may increase the mutation rate of a region compared to other genome regions. Besides, variants in specific sequence contexts like STRs may also have distinguished biological consequences, which can lead to certain human diseases and thus they may be used as biomarkers for disease diagnosis and treatments. Moreover, potential ambiguous variant representations such as equivalent or redundant indels are also related with their sequence contexts, which may complicate variant harmonization from different sources. Our previous study demonstrated that more than half of false positive indel calls detected through next generation sequencing data are related with STRs. Thus, the sequence contexts of genomic variants are important and cannot be ignored. However, the current methods or strategies for assessing the sequence contexts of genomic variants are limited and not feasible to use. Here, we developed a computational tool VarSCAT for sequence contexts annotation of genomic variants. Our tool provides diverse sequence contexts annotations providing users information to further explore the variants of their interests. By applying VarSCAT to high confidence human variant sets, we demonstrate the influence of sequence context of genomic variants and emphasize the importance of sequence context assessment.

Nonsense-mediated RNA decay (NMD) is a highly conserved and selective RNA turnover pathway that depends on the endonuclease SMG6. Here, we show that SMG6 is essential for male germ cell differentiation in mice. Germ-cell conditional knockout (cKO) of Smg6 induces extensive transcriptome misregulation, including a failure to eliminate meiotically expressed transcripts in early haploid cells, and accumulation of NMD target mRNAs with long 3′ untranslated regions (UTRs). Loss of SMG6 in the male germline results in complete arrest of spermatogenesis at the early haploid cell stage. We find that SMG6 is strikingly enriched in the chromatoid body (CB), a specialized cytoplasmic granule in male germ cells also harboring PIWI-interacting RNAs (piRNAs) and the piRNA-binding protein PIWIL1. This raises the possibility that SMG6 and the piRNA pathway function together, which is supported by several findings, including that Piwil1-KO mice phenocopy Smg6-cKO mice and that SMG6 and PIWIL1 co-regulate many genes in round spermatids. Together, our results demonstrate that SMG6 is an essential regulator of the male germline transcriptome, and highlight the CB as a molecular platform coordinating RNA regulatory pathways to control sperm production and fertility.

Single-cell RNA-sequencing enables cell-level investigation of cell differentiation, which can be modelled using trajectory inference methods. While tremendous effort has been put into designing these methods, inferring accurate trajectories automatically remains difficult. Therefore, the standard approach involves testing different trajectory inference methods and picking the trajectory giving the most biologically sensible model. As the default parameters are often suboptimal, their tuning requires methodological expertise. We introduce Totem, an open-source, easy-to-use R package designed to facilitate inference of tree-shaped trajectories from single-cell data. Totem generates a large number of clustering results, estimates their topologies as minimum spanning trees, and uses them to measure the connectivity of the cells. Besides automatic selection of an appropriate trajectory, cell connectivity enables to visually pinpoint branching points and milestones relevant to the trajectory. Furthermore, testing different trajectories with Totem is fast, easy, and does not require in-depth methodological knowledge.

BACKGROUND: Type 1 diabetes is a complex heterogenous autoimmune disease without therapeutic interventions available to prevent or reverse the disease. This study aimed to identify transcriptional changes associated with the disease progression in patients with recent-onset type 1 diabetes. METHODS: Whole-blood samples were collected as part of the INNODIA study at baseline and 12 months after diagnosis of type 1 diabetes. We used linear mixed-effects modelling on RNA-seq data to identify genes associated with age, sex, or disease progression. Cell-type proportions were estimated from the RNA-seq data using computational deconvolution. Associations to clinical variables were estimated using Pearson's or point-biserial correlation for continuous and dichotomous variables, respectively, using only complete pairs of observations. FINDINGS: We found that genes and pathways related to innate immunity were downregulated during the first year after diagnosis. Significant associations of the gene expression changes were found with ZnT8A autoantibody positivity. Rate of change in the expression of 16 genes between baseline and 12 months was found to predict the decline in C-peptide at 24 months. Interestingly and consistent with earlier reports, increased B cell levels and decreased neutrophil levels were associated with the rapid progression. INTERPRETATION: There is considerable individual variation in the rate of progression from appearance of type 1 diabetes-specific autoantibodies to clinical disease. Patient stratification and prediction of disease progression can help in developing more personalised therapeutic strategies for different disease endotypes. FUNDING: A full list of funding bodies can be found under Acknowledgments. ispartof: EBIOMEDICINE vol:92 ispartof: location:Netherlands status: published

Basal-like breast cancers (BLBC) are characterized by defects in homologous recombination (HR), deficient mitotic checkpoint, and high-proliferation activity. Here, we discover CIP2A as a candidate driver of BLBC. CIP2A was essential for DNA damage–induced initiation of mouse BLBC-like mammary tumors and for survival of HR–defective BLBC cells. CIP2A was dispensable for normal mammary gland development and for unperturbed mitosis, but selectively essential for mitotic progression of DNA damaged cells. A direct interaction between CIP2A and a DNA repair scaffold protein TopBP1 was identified, and CIP2A inhibition resulted in enhanced DNA damage–induced TopBP1 and RAD51 recruitment to chromatin in mammary epithelial cells. In addition to its role in tumor initiation, and survival of BRCA-deficient cells, CIP2A also drove proliferative MYC and E2F1 signaling in basal-like triple-negative breast cancer (BL-TNBC) cells. Clinically, high CIP2A expression was associated with poor patient prognosis in BL-TNBCs but not in other breast cancer subtypes. Small-molecule reactivators of PP2A (SMAP) inhibited CIP2A transcription, phenocopied the CIP2A-deficient DNA damage response (DDR), and inhibited growth of patient-derived BLBC xenograft. In summary, these results demonstrate that CIP2A directly interacts with TopBP1 and coordinates DNA damage–induced mitotic checkpoint and proliferation, thereby driving BLBC initiation and progression. SMAPs could serve as a surrogate therapeutic strategy to inhibit the oncogenic activity of CIP2A in BLBCs. Significance: These results identify CIP2A as a nongenetic driver and therapeutic target in basal-like breast cancer that regulates DNA damage–induced G2–M checkpoint and proliferative signaling.

Motivation The emergence of datasets with tens of thousands of features, such as high-throughput omics biomedical data, highlights the importance of reducing the feature space into a distilled subset that can truly capture the signal for research and industry by aiding in finding more effective biomarkers for the question in hand. A good feature set also facilitates building robust predictive models with improved interpretability and convergence of the applied method due to the smaller feature space. Results Here, we present a robust feature selection method named Stable Iterative Variable Selection (SIVS) and assess its performance over both omics and clinical data types. As a performance assessment metric, we compared the number and goodness of the selected feature using SIVS to those selected by Least Absolute Shrinkage and Selection Operator regression. The results suggested that the feature space selected by SIVS was, on average, 41% smaller, without having a negative effect on the model performance. A similar result was observed for comparison with Boruta and caret RFE. Availability and implementation The method is implemented as an R package under GNU General Public License v3.0 and is accessible via Comprehensive R Archive Network (CRAN) via https://cran.r-project.org/package=sivs. Supplementary information Supplementary data are available at Bioinformatics online.

Brown adipose tissue (BAT) thermogenesis is activated by feeding. Recently, we revealed a secretin-mediated gut-BAT-brain axis, which stimulates satiation in mice, but the purpose of meal-induced BAT activation in humans has been unclear. In this placebo-controlled, randomized crossover study, we investigated the effects of intravenous secretin on BAT metabolism (measured with [18F]FDG and [15O]H2O positron emission tomography) and appetite (measured with functional magnetic resonance imaging) in healthy, normal weight men (GUTBAT trial no. NCT03290846). Participants were blinded to the intervention. Secretin increased BAT glucose uptake (primary endpoint) compared to placebo by 57% (median (interquartile range, IQR), 0.82 (0.77) versus 0.59 (0.53) μmol per 100 g per min, 95% confidence interval (CI) (0.09, 0.89), P = 0.002, effect size r = 0.570), while BAT perfusion remained unchanged (mean (s.d.) 4.73 (1.82) versus 6.14 (3.05) ml per 100 g per min, 95%CI (-2.91, 0.07), P = 0.063, effect size d = -0.549) (n = 15). Whole body energy expenditure increased by 2% (P = 0.011) (n = 15). Secretin attenuated blood-oxygen level-dependent activity (primary endpoint) in brain reward circuits during food cue tasks (significance level false discovery rate corrected at P = 0.05) (n = 14). Caloric intake did not significantly change, but motivation to refeed after a meal was delayed by 39 min (P = 0.039) (n = 14). No adverse effects were detected. Here we show in humans that secretin activates BAT, reduces central responses to appetizing food and delays the motivation to refeed after a meal. This suggests that meal-induced, secretin-mediated BAT activation is relevant in the control of food intake in humans. As obesity is increasing worldwide, this appetite regulating axis offers new possibilities for clinical research in treating obesity.

Single-cell RNA-sequencing (scRNA-seq) enables researchers to quantify transcriptomes of thousands of cells simultaneously and study transcriptomic changes between cells. scRNA-seq datasets increasingly include multisubject, multicondition experiments to investigate cell-type-specific differential states (DS) between conditions. This can be performed by first identifying the cell types in all the subjects and then by performing a DS analysis between the conditions within each cell type. Naïve single-cell DS analysis methods that treat cells statistically independent are subject to false positives in the presence of variation between biological replicates, an issue known as the pseudoreplicate bias. While several methods have already been introduced to carry out the statistical testing in multisubject scRNA-seq analysis, comparisons that include all these methods are currently lacking. Here, we performed a comprehensive comparison of 18 methods for the identification of DS changes between conditions from multisubject scRNA-seq data. Our results suggest that the pseudobulk methods performed generally best. Both pseudobulks and mixed models that model the subjects as a random effect were superior compared with the naïve single-cell methods that do not model the subjects in any way. While the naïve models achieved higher sensitivity than the pseudobulk methods and the mixed models, they were subject to a high number of false positives. In addition, accounting for subjects through latent variable modeling did not improve the performance of the naïve methods.

The randomized controlled Special Turku Coronary Risk Factor Intervention Project (STRIP) has completed a 20-year infancy-onset dietary counselling intervention to reduce exposure to atherosclerotic cardiovascular disease risk factors via promotion of a heart-healthy diet. The counselling on, e.g., low intake of saturated fat and cholesterol and promotion of fruit, vegetable, and whole-grain consumption has affected the dietary characteristics of the intervention participants. By leveraging this unique cohort, we further investigated whether this long-term dietary intervention affected the gut microbiota bacterial profile six years after the intervention ceased. Our sub-study comprised 357 individuals aged 26 years (intervention n = 174, control n = 183), whose gut microbiota were profiled using 16S rRNA amplicon sequencing. We observed no differences in microbiota profiles between the intervention and control groups. However, out of the 77 detected microbial genera, the Veillonella genus was more abundant in the intervention group compared to the controls (log2 fold-change 1.58, p < 0.001) after adjusting for multiple comparison. In addition, an association between the study group and overall gut microbiota profile was found only in males. The subtle differences in gut microbiota abundances observed in this unique intervention setting suggest that long-term dietary counselling reflecting dietary guidelines may be associated with alterations in gut microbiota.

Purpose: Cancerous inhibitor of protein phosphatase 2A (CIP2A) is an oncoprotein that inhibits the tumor suppressor PP2A-B56α. However, CIP2A mRNA variants remain uncharacterized. Here, we report the discovery of a CIP2A splicing variant, novel CIP2A variant (NOCIVA). Experimental Design: Characterization of CIP2A variants was performed by both 3′ and 5′ rapid amplification of cDNA ends from cancer cells. The function of NOCIVA was assessed by structural and molecular biology approaches. Its clinical relevance was studied in an acute myeloid leukemia (AML) patient cohort and two independent chronic myeloid leukemia (CML) cohorts. Results: NOCIVA contains CIP2A exons 1 to 13 fused to 349 nucleotides from CIP2A intron 13. Intriguingly, the first 39 nucleotides of the NOCIVA-specific sequence are in the coding frame with exon 13 of CIP2A and code for a 13-amino acid peptide tail nonhomologous to any known human protein sequence. Therefore, NOCIVA translates to a unique human protein. NOCIVA retains the capacity to bind to B56α, but, whereas CIP2A is predominantly a cytoplasmic protein, NOCIVA translocates to the nucleus. Indicative of prevalent alternative splicing from CIP2A to NOCIVA in myeloid malignancies, AML and CML patient samples overexpress NOCIVA, but not CIP2A mRNA. In AML, a high NOCIVA/CIP2A mRNA expression ratio is a marker for adverse overall survival. In CML, high NOCIVA expression is associated with inferior event-free survival among imatinib-treated patients, but not among patients treated with dasatinib or nilotinib. Conclusions: We discovered a novel variant of the oncoprotein CIP2A and its clinical relevance in predicting tyrosine kinase inhibitor therapy resistance in myeloid leukemias.

Single-cell omics technologies are currently solving biological and medical problems that earlier have remained elusive, such as discovery of new cell types, cellular differentiation trajectories and communication networks across cells and tissues. Current advances especially in single-cell multi-omics hold high potential for breakthroughs by integration of multiple different omics layers. To pair with the recent biotechnological developments, many computational approaches to process and analyze single-cell multi-omics data have been proposed. In this review, we first introduce recent developments in single-cell multi-omics in general and then focus on the available data integration strategies. The integration approaches are divided into three categories: early, intermediate, and late data integration. For each category, we describe the underlying conceptual principles and main characteristics, as well as provide examples of currently available tools and how they have been applied to analyze single-cell multi-omics data. Finally, we explore the challenges and prospective future directions of single-cell multi-omics data integration, including examples of adopting multi-view analysis approaches used in other disciplines to single-cell multi-omics.

Motivation Single-cell RNA-seq allows researchers to identify cell populations based on unsupervised clustering of the transcriptome. However, subpopulations can have only subtle transcriptomic differences and the high dimensionality of the data makes their identification challenging. Results We introduce ILoReg, an R package implementing a new cell population identification method that improves identification of cell populations with subtle differences through a probabilistic feature extraction step that is applied before clustering and visualization. The feature extraction is performed using a novel machine learning algorithm, called iterative clustering projection (ICP), that uses logistic regression and clustering similarity comparison to iteratively cluster data. Remarkably, ICP also manages to integrate feature selection with the clustering through L1-regularization, enabling the identification of genes that are differentially expressed between cell populations. By combining solutions of multiple ICP runs into a single consensus solution, ILoReg creates a representation that enables investigating cell populations with a high resolution. In particular, we show that the visualization of ILoReg allows segregation of immune and pancreatic cell populations in a more pronounced manner compared with current state-of-the-art methods. Availability and implementation ILoReg is available as an R package at https://bioconductor.org/packages/ILoReg. Supplementary information Supplementary data are available at Bioinformatics online.

The randomized controlled Special Turku Coronary Risk Factor Intervention Project (STRIP) has completed a 20-year infancy-onset dietary counselling intervention to reduce exposure to atherosclerotic cardiovascular disease risk factors via promotion of a heart-healthy diet. The counselling on, e.g., low intake of saturated fat and cholesterol and promotion of fruit, vegetable, and whole-grain consumption has affected the dietary characteristics of the intervention participants. By leveraging this unique cohort, we further investigated whether this long-term dietary intervention affected the gut microbiota bacterial profile six years after the intervention ceased. Our sub-study comprised 357 individuals aged 26 years (intervention

Funding Information: Open Access funding provided by University of Turku (UTU) including Turku University Central Hospital. R Lahesmaa was supported by the Academy of Finland (AoF) (grants 292335, 294337, 319280, 31444, 319280, 329277, 331790) and Business Finland, and grants from JDRF, the Novo Nordisk Foundation (grant NNF19OC0057218), the Sigrid Jusélius Foundation (SJF), the Jane and Aatos Erkko Foundation, the Finnish Diabetes Foundation and the Finnish Cancer Foundation. R Lahesmaa, HL and MK were supported by the AoF Centre of Excellence in Molecular Systems Immunology and Physiology Research (2012–2017), grant 250114 and grant 292482. VT was supported by the Estonian Research Council (grant PRG1428). LLE reports grants from the European Research Council (ERC) (677943), the European Union’s Horizon 2020 research and innovation programme (955321), the AoF (296801, 310561, 314443, 329278, 335434 and 335611) and the SJF. Our research is also supported by the University of Turku Graduate School (UTUGS), Biocenter Finland and the InFLAMES Flagship Programme of the AoF (decision number: 337530). IS was supported by the Turku Doctoral Programme of Molecular Medicine (TuDMM) and the Finnish Diabetes Research Foundation. EL was supported by the TuDMM, the Finnish Cultural Foundation, and the Kyllikki and Uolevi Lehikoinen Foundation. TG was supported by the Academy of Finland (grant 340231). Funding Information: We are grateful to the families for their participation in the DIABIMMUNE study and the clinical personnel for excellent collaboration, work with the families and collection of the samples for the study. We thank M. Hakkarainen and S. Heinonen (Turku Bioscience Centre, University of Turku, Finland) for their excellent technical help. We acknowledge the Turku Bioscience Centre’s core facility, the Finnish Functional Genomics Centre (FFGC), supported by Biocenter Finland, for their assistance. We also acknowledge the Finnish Centre for Scientific Computing (CSC), Elixir Finland and the Aalto Science-IT project for computational resources. The graphical abstract was created with BioRender. Publisher Copyright: © 2022, The Author(s). Aims/hypothesis: Type 1 diabetes is a chronic autoimmune disease of complex aetiology, including a potential role for epigenetic regulation. Previous epigenomic studies focused mainly on clinically diagnosed individuals. The aim of the study was to assess early DNA methylation changes associated with type 1 diabetes already before the diagnosis or even before the appearance of autoantibodies. Methods: Reduced representation bisulphite sequencing (RRBS) was applied to study DNA methylation in purified CD4+ T cell, CD8+ T cell and CD4−CD8− cell fractions of 226 peripheral blood mononuclear cell samples longitudinally collected from seven type 1 diabetes-specific autoantibody-positive individuals and control individuals matched for age, sex, HLA risk and place of birth. We also explored correlations between DNA methylation and gene expression using RNA sequencing data from the same samples. Technical validation of RRBS results was performed using pyrosequencing. Results: We identified 79, 56 and 45 differentially methylated regions in CD4+ T cells, CD8+ T cells and CD4−CD8− cell fractions, respectively, between type 1 diabetes-specific autoantibody-positive individuals and control participants. The analysis of pre-seroconversion samples identified DNA methylation signatures at the very early stage of disease, including differential methylation at the promoter of IRF5 in CD4+ T cells. Further, we validated RRBS results using pyrosequencing at the following CpG sites: chr19:18118304 in the promoter of ARRDC2; chr21:47307815 in the intron of PCBP3; and chr14:81128398 in the intergenic region near TRAF3 in CD4+ T cells. Conclusions/interpretation: These preliminary results provide novel insights into cell type-specific differential epigenetic regulation of genes, which may contribute to type 1 diabetes pathogenesis at the very early stage of disease development. Should these findings be validated, they may serve as a potential signature useful for disease prediction and management. Graphical abstract: [Figure not available: see fulltext.]

The coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading across the world despite vast global vaccination efforts. Consequently, many studies have looked for potential human host factors and immune mechanisms associated with the disease. However, most studies have focused on comparing COVID-19 patients to healthy controls, while fewer have elucidated the specific host factors distinguishing COVID-19 from other infections. To discover genes specifically related to COVID-19, we reanalyzed transcriptome data from nine independent cohort studies, covering multiple infections, including COVID-19, influenza, seasonal coronaviruses, and bacterial pneumonia. The identified COVID-19-specific signature consisted of 149 genes, involving many signals previously associated with the disease, such as induction of a strong immunoglobulin response and hemostasis, as well as dysregulation of cell cycle-related processes. Additionally, potential new gene candidates related to COVID-19 were discovered. To facilitate exploration of the signature with respect to disease severity, disease progression, and different cell types, we also offer an online tool for easy visualization of the selected genes across multiple datasets at both bulk and single-cell levels.

Transcriptome level expression data connected to the spatial organization of the cells and molecules would allow a comprehensive understanding of how gene expression is connected to the structure and function in the biological systems. The spatial transcriptomics platforms may soon provide such information. However, the current platforms still lack spatial resolution, capture only a fraction of the transcriptome heterogeneity, or lack the throughput for large scale studies. The strengths and weaknesses in current ST platforms and computational solutions need to be taken into account when planning spatial transcriptomics studies. The basis of the computational ST analysis is the solutions developed for single-cell RNA-sequencing data, with advancements taking into account the spatial connectedness of the transcriptomes. The scRNA-seq tools are modified for spatial transcriptomics or new solutions like deep learning-based joint analysis of expression, spatial, and image data are developed to extract biological information in the spatially resolved transcriptomes. The computational ST analysis can reveal remarkable biological insights into spatial patterns of gene expression, cell signaling, and cell type variations in connection with cell type-specific signaling and organization in complex tissues. This review covers the topics that help choosing the platform and computational solutions for spatial transcriptomics research. We focus on the currently available ST methods and platforms and their strengths and limitations. Of the computational solutions, we provide an overview of the analysis steps and tools used in the ST data analysis. The compatibility with the data types and the tools provided by the current ST analysis frameworks are summarized.

Th17 cells are essential for protection against extracellular pathogens, but their aberrant activity can cause autoimmunity. Molecular mechanisms that dictate Th17 cell-differentiation have been extensively studied using mouse models. However, species-specific differences underscore the need to validate these findings in human. Here, we characterized the human-specific roles of three AP-1 transcription factors, FOSL1, FOSL2 and BATF, during early stages of Th17 differentiation. Our results demonstrate that FOSL1 and FOSL2 co-repress Th17 fate-specification, whereas BATF promotes the Th17 lineage. Strikingly, FOSL1 was found to play different roles in human and mouse. Genome-wide binding analysis indicated that FOSL1, FOSL2 and BATF share occupancy over regulatory regions of genes involved in Th17 lineage commitment. These AP-1 factors also share their protein interacting partners, which suggests mechanisms for their functional interplay. Our study further reveals that the genomic binding sites of FOSL1, FOSL2 and BATF harbour hundreds of autoimmune disease-linked SNPs. We show that many of these SNPs alter the ability of these transcription factors to bind DNA. Our findings thus provide critical insights into AP-1-mediated regulation of human Th17-fate and associated pathologies.

The use of glucocorticoids has given contradictory results for treating acute respiratory distress syndrome (ARDS). Here we report a novel disease association of a SNP rs9984273, which is situated in the interferon alpha/beta receptor (IFNAR2) gene in an area corresponding to a binding motif of the glucocorticoid receptor (GR). The minor allele of SNP rs9984273 associates with higher IFNAR expression, lower IFN-gamma and IL-6 levels and less severe form of coronavirus diseases (COVID-19) according to the COVID-19 Host Genetics Initiative database, and better outcome in interferon (IFN) beta treated patients with ARDS. Thus, the distribution of this SNP within clinical study arms may explain the contradictory results of multiple ARDS studies and outcomes in COVID-19 concerning type I IFN signalling and glucocorticoids.One-Sentence SummarySingle nucleotide polymorphism in interferon receptor contributes to corticosteroid response and outcome in ARDS and COVID-19

Ductal carcinoma in situ (DCIS) is a pre-invasive stage of breast cancer. During invasion, the encapsulating DCIS basement membrane (BM) is compromised, and tumor cells invade the surrounding stroma. The mechanisms that regulate functional epithelial BMs in vivo are poorly understood. Myosin-X (MYO10) is a filopodia-inducing protein associated with metastasis and poor clinical outcome in invasive breast cancer (IBC). We identify elevated MYO10 expression in human DCIS and IBC, and this suggests links with disease progression. MYO10 promotes filopodia formation and cell invasion in vitro and cancer-cell dissemination from progressively invasive human DCIS xenografts. However, MYO10-depleted xenografts are more invasive. These lesions exhibit compromised BMs, poorly defined borders, and increased cancer-cell dispersal and EMT-marker-positive cells. In addition, cancer spheroids are dependent on MYO10-filopodia to generate a near-continuous extracellular matrix boundary. Thus, MYO10 is protective in early-stage breast cancer, correlating with tumor-limiting BMs, and pro-invasive at later stages, facilitating cancer-cell dissemination. ispartof: DEVELOPMENTAL CELL vol:57 issue:20 pages:2350-+ ispartof: location:United States status: published

In brief Preeclampsia is a common serious disorder that can occur during pregnancy. This study uses integrative analysis of preeclampsia transcriptomes and single-cell transcriptomes to predict cell type-specific contributions to preeclampsia. Abstract Preeclampsia is a devastating pregnancy disorder and a major cause of maternal and perinatal mortality. By combining previous transcriptomic results on preeclampsia with single-cell sequencing data, we here predict distinct and partly unanticipated contributions of decidual stromal cells and uterine natural killer cells in early- and late-onset preeclampsia.

Human reproductive success depends on a properly decidualized uterine endometrium that allows implantation and the formation of the placenta. At the core of the decidualization process are endometrial stromal fibroblasts (ESF) that differentiate to decidual stromal cells (DSC). As variations in oxygen levels are functionally relevant in endometrium both upon menstruation and during placentation, we assessed the transcriptomic responses to hypoxia in ESF and DSC. In both cell types, hypoxia-upregulated genes in classical hypoxia pathways such as glycolysis and the epithelial mesenchymal transition. In DSC, hypoxia restored an ESF-like transcriptional state for a subset of transcription factors that are known targets of the progesterone receptor, suggesting that hypoxia partially interferes with progesterone signaling. In both cell types, hypoxia modified transcription of several inflammatory transcription factors that are known regulators of decidualization, including decreased transcription of STATs and increased transcription of CEBPs. We observed that hypoxia-upregulated genes in ESF and DSC had a significant overlap with genes previously detected to be upregulated in endometriotic stromal cells. Promoter analysis of the genes in this overlap suggested the hypoxia-upregulated Jun/Fos and CEBP transcription factors as potential drivers of endometriosis-associated transcription. Using immunohistochemistry, we observed increased expression of JUND and CEBPD in endometriosis lesions compared to healthy endometria. Overall, the findings suggest that hypoxic stress establishes distinct transcriptional states in ESF and DSC and that hypoxia influences the expression of genes that contribute to the core gene regulation of endometriotic stromal cells.

BACKGROUND: Patient-derived xenografts (PDXs) are considered to better recapitulate the histopathological and molecular heterogeneity of human cancer than other preclinical models. Despite technological advances, PDX models from hormone naïve primary prostate cancer are scarce. We performed a detailed analysis of PDX methodology using a robust subcutaneous model and fresh tissues from patients with primary hormone naïve prostate cancer. METHODS: Clinical prostate tumor specimens (n=26, Gleason score 6–10) were collected from robotic-assisted laparoscopic radical prostatectomies at Turku University Hospital (Turku, Finland), cut into pieces, and implanted subcutaneously into 84 immunodeficient mice. Engraftments and the adjacent material from prostatic surgical specimens were compared using histology, immunohistochemistry and DNA sequencing. RESULTS: The probability of a successful engraftment correlated with the presence of carcinoma in the implanted tissue. Tumor take rate was 41%. Surprisingly, mouse hormone supplementation inhibited tumor take rate, whereas the degree of mouse immunodeficiency did not have an effect. Histologically, the engrafted tumors closely mimicked their parental tumors, and the Gleason grades and copy number variants of the engraftments were similar to those of their primary tumors. Expression levels of androgen receptor, prostate-specific antigen, and keratins were retained in engraftments, and a detailed genomic analysis revealed high fidelity of the engraftments with their corresponding primary tumors. However, in the second or third passage of tumors, the carcinoma areas were almost completely replaced by benign tissue with frequent degenerative or metaplastic changes. CONCLUSIONS: Subcutaneous primary prostate engraftments preserve the phenotypic and genotypic landscape. Thus, they serve a potential model for personalized medicine and preclinical research but their use may be limited to the first passage.

Objective The Systolic Blood Pressure Intervention Trial (SPRINT) reported that lowering SBP to below 120 mmHg (intensive treatment) reduced cardiovascular morbidity and mortality among adults with hypertension but increased the incidence of adverse events, particularly acute kidney injury (AKI). The goal of this study was to develop an accurate risk estimation tool for comparing the risk of cardiovascular events and adverse kidney-related outcomes between standard and intensive antihypertensive treatment strategies. Methods By applying Lasso regression on the baseline characteristics and health outcomes of 8760 participants with complete baseline information in the SPRINT trial, we developed predictive models for primary cardiovascular disease (CVD) outcome and incidence of AKI. Both models were validated against an independent test set of the SPRINT trial (one third of data not used for model building) and externally against the cardiovascular and renal outcomes available in Action to Control Cardiovascular Risk in Diabetes Blood Pressure trial, consisting of 4733 participants with type 2 diabetes mellitus. Results Lasso regression identified a subset of variables that accurately predicted the primary CVD outcome and the incidence of AKI (areas under receiver-operating characteristic curves 0.70 and 0.77, respectively). Based on the validated risk models, an easy-to-use risk assessment tool was developed and made available as an easy-to-use online tool. Conclusion By predicting the risks of CVD and AKI at baseline, the developed tool can be used to weigh the benefits of intensive versus standard blood pressure control and to identify those who are likely to benefit most from intensive treatment.

Machine learning methods have gained increased popularity in biomedical research during the recent years. However, very few of them support the analysis of longitudinal data, where several samples are collected from an individual over time. Additionally, most of the available longitudinal machine learning methods assume that the measurements are aligned in time, which is often not the case in real data. Here, we introduce a robust longitudinal machine learning method, named likelihood contrasts (LC), which supports study designs with unaligned time points. Our LC method is a binary classifier, which uses linear mixed models for modelling and log-likelihood for decision making. To demonstrate the benefits of our approach, we compared it with existing methods in four simulated and three real data sets. In each simulated data set, LC was the most accurate method, while the real data sets further supported the robust performance of the method. LC is also computationally efficient and easy to use.

Summary: Forkhead box protein P3+ (FOXP3+) regulatory T cells (Treg cells) play a key role in maintaining tolerance and immune homeostasis. Here, we report that a T cell-specific deletion of the transcription factor MAZR (also known as PATZ1) leads to an increased frequency of Treg cells, while enforced MAZR expression impairs Treg cell differentiation. Further, MAZR expression levels are progressively downregulated during thymic Treg cell development and during in-vitro-induced human Treg cell differentiation, suggesting that MAZR protein levels are critical for controlling Treg cell development. However, MAZR-deficient Treg cells show only minor transcriptional changes ex vivo, indicating that MAZR is not essential for establishing the transcriptional program of peripheral Treg cells. Finally, the loss of MAZR reduces the clinical score in dextran-sodium sulfate (DSS)-induced colitis, suggesting that MAZR activity in T cells controls the extent of intestinal inflammation. Together, these data indicate that MAZR is part of a Treg cell-intrinsic transcriptional network that modulates Treg cell development. : FOXP3+ Treg cells are essential for maintaining tolerance and immune homeostasis. Andersen et al. reveal that MAZR is an important factor in regulating the delicate balance of Treg cell generation and report that MAZR expression levels play a key role in controlling Treg cell development and differentiation. Keywords: MAZR, PATZ1, FOXP3, regulatory T cells, Treg, DSS-induced colitis

Background: The existing risk prediction models for chemotherapy-induced febrile neutropenia (FN) do not necessarily apply to real-life patients in different healthcare systems and the external validation of these models are often lacking. Our study evaluates whether a machine learning-based risk prediction model could outperform the previously introduced models, especially when validated against real-world patient data from another institution not used for model training. Methods: Using Turku University Hospital electronic medical records, we identified all patients who received chemotherapy for non-hematological cancer between the years 2010 and 2017 (N = 5879). An experimental surrogate endpoint was first-cycle neutropenic infection (NI), defined as grade IV neutropenia with serum C-reactive protein >10 mg/l. For predicting the risk of NI, a penalized regression model (Lasso) was developed. The model was externally validated in an independent dataset (N = 4594) from Tampere University Hospital. Results: Lasso model accurately predicted NI risk with good accuracy (AUROC 0.84). In the validation cohort, the Lasso model outperformed two previously introduced, widely approved models, with AUROC 0.75. The variables selected by Lasso included granulocyte colony-stimulating factor (G-CSF) use, cancer type, pre-treatment neutrophil and thrombocyte count, intravenous treatment regimen, and the planned dose intensity. The same model predicted also FN, with AUROC 0.77, supporting the validity of NI as an endpoint. Conclusions: Our study demonstrates that real-world NI risk prediction can be improved with machine learning and that every difference in patient or treatment characteristics can have a significant impact on model performance. Here we outline a novel, externally validated approach which may hold potential to facilitate more targeted use of G-CSFs in the future. publishedVersion

Mass spectrometry proteomics has become an important part of modern immunology, making major contributions to understanding protein expression levels, subcellular localizations, posttranslational modifications, and interactions in various immune cell populations. New developments in both experimental and computational techniques offer increasing opportunities for exploring the immune system and the molecular mechanisms involved in immune responses. Here, we focus on current computational approaches to infer relevant information from large mass spectrometry based protein profiling datasets, covering the different steps of the analysis from protein identification and quantification to further mining and modelling of the protein abundance data. Additionally, we provide a summary of the key proteome profiling studies on human CD4

Ductal carcinoma in situ (DCIS) is a pre-invasive stage of breast cancer, where the tumor is encapsulated by a basement membrane (BM). At the invasive phase, the BM barrier is compromised enabling tumor cells to escape into the surrounding stroma. The molecular mechanisms that establish and maintain an epithelial BM barrier in vivo are poorly understood. Myosin-X (MYO10) is a filopodia-inducing motor protein implicated in metastasis and poor clinical outcome in patients with invasive breast cancer (IBC). We compared MYO10 expression in patient-matched normal breast tissue and DCIS lesions and found elevated MYO10 expression in DCIS samples, suggesting that MYO10 might facilitate the transition from DCIS to IBC. Indeed, MYO10 promoted the formation of filopodia and cell invasion in vitro and positively regulated the dissemination of individual cancer cells from IBC lesions in vivo. However, MYO10-depleted DCIS xenografts were, unexpectedly, more invasive. In these xenografts, MYO10 depletion compromised BM formation around the lesions resulting in poorly defined tumor borders and increased cancer cell dispersal into the surrounding stroma. Moreover, MYO10-depleted tumors showed increased EMT-marker-positive cells, specifically at the tumor periphery. We also observed cancer spheroids undergoing rotational motion and recruiting BM components in a filopodia-dependent manner to generate a near-continuous extracellular matrix boundary. Taken together, our data identify a protective role for MYO10 in early-stage breast cancer, where MYO10-dependent tumor cell protrusions support BM assembly at the tumor-stroma interface to limit cancer progression, and a pro-invasive role that facilitates cancer cell dissemination at later stages.Abstract FigureHighlights-Filopodia sculpt the tumor-proximal stroma in pre-invasive ductal carcinoma in situ (DCIS).-Filopodia-dependent basement membrane (BM) assembly limits invasive transition of DCIS-like tumors in vivo.-Loss of MYO10-dependent filopodia impairs BM assembly and induces an EMT-like phenotype at the tumor-stroma interface in vivo.-MYO10 filopodia are anti-invasive in DCIS but facilitate dissemination in invasive breast cancer.