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Background: Bladder cancer is a highly over-represented disease in males. The involvement of sex steroids in bladder carcinogenesis and the utilisation of steroid hormone action as a therapeutic target have been frequently proposed. However, the intratumoural steroid milieu remains unclear., Methods: We used mass spectrometry and transcriptomic profiling to determine the levels of 23 steroid hormones and the expression of steroidogenic enzymes in primary tumours from patients who underwent transurethral resection (n = 24), and tumours and adjacent morphologically benign bladder tissues from treatment-naïve patients, who underwent radical cystectomy (n = 20). The corresponding steroids were determined from the patients' sera., Findings: Our results show that both bladder tumours and non-tumour tissues are androgen-poor, with DHT being virtually unquantifiable and testosterone at castration levels. Intratumoural enzymes that inactivate potent androgens (e.g., HSD17B2) exhibited similar tumour aggressiveness-linked downregulation, as reported in advanced forms of classical steroid-dependent cancers, whereas there was little change in the corresponding activating enzymes. Finally, our results suggest cancer aggressiveness-linked dissimilarities in steroid profiles; the patients with overall low circulating steroid levels and those with an association between androgen receptor expression and intratumoural testosterone levels in place had fewer recurrences than the rest., Interpretation: By revealing the steroid landscape of bladder cancer, our study not only underscores the androgen-poor nature of the malignancy but also identifies potential alterations in steroid profiles that are linked to disease aggressiveness., Funding: The Cancer Foundation Finland, the Finnish State Research Funding (VTR)., Competing Interests: Declaration of interests JM reports funding for PhD studies from the University of Turku Graduate School. Other authors declare no potential conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Objective: Preoperative risk prediction models can support shared decision-making before total hip arthroplasties (THAs). Here, we compare different machine-learning (ML) approaches to predict the six-month risk of adverse events following primary THA to obtain accurate yet simple-to-use risk prediction models., Methods: We extracted data on primary THAs (N = 262,356) between 2010 and 2018 from the Nordic Arthroplasty Register Association dataset. We benchmarked a variety of ML algorithms in terms of the area under the receiver operating characteristic curve (AUROC) for predicting the risk of revision caused by periprosthetic joint infection (PJI), dislocation or periprosthetic fracture (PPF), and death. All models were internally validated against a randomly selected test cohort (one-third of the data) that was not used for training the models., Results: The incidences of revisions because of PJI, dislocation, and PPF were 0.8%, 0.4%, and 0.3%, respectively, and the incidence of death was 1.2%. Overall, Lasso regression with stable iterative variable selection (SIVS) produced models using only four to five input variables but with AUROC comparable to more complex models using all 32 variables available. The SIVS-based Lasso models based on age, sex, preoperative diagnosis, bearing couple, fixation, and surgical approach predicted the risk of revisions caused by PJI, dislocations, and PPF, as well as death, with AUROCs of 0.61, 0.67, 0.76, and 0.86, respectively., Conclusion: Our study demonstrates that satisfactory predictive potential for adverse events following THA can be reached with parsimonious modeling strategies. The SIVS-based Lasso models may serve as simple-to-use tools for clinical risk assessment in the future., (© 2024 The Author(s). ACR Open Rheumatology published by Wiley Periodicals LLC on behalf of American College of Rheumatology.)

Human reproductive success relies on the proper differentiation of the uterine endometrium to facilitate implantation, formation of the placenta, and pregnancy. This process involves two critical types of decidual uterine cells: endometrial/decidual stromal cells (dS) and uterine/decidual natural killer (dNK) cells. To better understand the transcription factors governing the in vivo functions of these cells, we analyzed single-cell transcriptomics data from first-trimester terminations of pregnancy, and for the first time conducted gene regulatory network analysis of dS and dNK cell subpopulations. Our analysis revealed stromal cell populations that corresponded to previously described in vitro decidualized cells and senescent decidual cells. We discovered new decidualization driving transcription factors of stromal cells for early pregnancy, including DDIT3 and BRF2, which regulate oxidative stress protection. For dNK cells, we identified transcription factors involved in the immunotolerant (dNK1) subpopulation, including IRX3 and RELB, which repress the NFKB pathway. In contrast, for the less immunotolerant (dNK3) population we predicted TBX21 (T-bet) and IRF2-mediated upregulation of the interferon pathway. To determine the clinical relevance of our findings, we tested the overrepresentation of the predicted transcription factors target genes among cell type-specific regulated genes from pregnancy disorders, such as recurrent pregnancy loss and preeclampsia. We observed that the predicted decidualized stromal and dNK1-specific transcription factor target genes were enriched with the genes downregulated in pregnancy disorders, whereas the predicted dNK3-specific targets were enriched with genes upregulated in pregnancy disorders. Our findings emphasize the importance of stress tolerance pathways in stromal cell decidualization and immunotolerance promoting regulators in dNK differentiation., (© 2024. The Author(s).)

The function of hydroxysteroid dehydrogenase 12 (HSD17B12) in lipid metabolism is poorly understood. To study this further, we created mice with hepatocyte-specific knockout of HSD17B12 (LiB12cKO). From 2 months on, these mice showed significant fat accumulation in their liver. As they aged, they also had a reduced whole-body fat percentage. Interestingly, the liver fat accumulation did not result in the typical formation of large lipid droplets (LD); instead, small droplets were more prevalent. Thus, LiB12KO liver did not show increased macrovesicular steatosis with the increasing fat content, while microvesicular steatosis was the predominant feature in the liver. This indicates a failure in the LD expansion. This was associated with liver damage, presumably due to lipotoxicity. Notably, the lipidomics data did not support an essential role of HSD17B12 in fatty acid (FA) elongation. However, we did observe a decrease in the quantity of specific lipid species that contain FAs with carbon chain lengths of 18 and 20 atoms, including oleic acid. Of these, phosphatidylcholine and phosphatidylethanolamine have been shown to play a key role in LD formation, and a limited amount of these lipids could be part of the mechanism leading to the dysfunction in LD expansion. The increase in the Cidec expression further supported the deficiency in LD expansion in the LiB12cKO liver. This protein is crucial for the fusion and growth of LDs, along with the downregulation of several members of the major urinary protein family of proteins, which have recently been shown to be altered during endoplasmic reticulum stress., (© 2024 The Author(s). The FASEB Journal published by Wiley Periodicals LLC on behalf of Federation of American Societies for Experimental Biology.)

Regulatory T cells (Tregs) play a key role in suppressing systemic effector immune responses, thereby preventing autoimmune diseases but also potentially contributing to tumor progression. Thus, there is great interest in clinically manipulating Tregs, but the precise mechanisms governing in vitro-induced Treg (iTreg) differentiation are not yet fully understood. Here, we used multiparametric mass cytometry to phenotypically profile human iTregs during the early stages of in vitro differentiation at single-cell level. A panel of 25 metal-conjugated antibodies specific to markers associated with human Tregs was used to characterize these immunomodulatory cells. We found that iTregs highly express the transcription factor FOXP3, as well as characteristic Treg-associated surface markers (e.g. CD25, PD1, CD137, CCR4, CCR7, CXCR3, and CD103). Expression of co-inhibitory factors (e.g. TIM3, LAG3, and TIGIT) increased slightly at late stages of iTreg differentiation. Further, CD103 was upregulated on a subpopulation of iTregs with greater suppressive capacity than their CD103 - counterparts. Using mass-spectrometry-based proteomics, we showed that sorted CD103 + iTregs express factors associated with immunosuppression. Overall, our study highlights that during early stages of differentiation, iTregs resemble memory-like Treg features with immunosuppressive activity, and provides opportunities for further investigation into the molecular mechanisms underlying Treg function., (© 2024. The Author(s).)

Hydroxysteroid (17beta) dehydrogenase 1 (HSD17B1) is a steroid synthetic enzyme expressed in ovarian granulosa cells and placental syncytiotrophoblasts. Here, HSD17B1 serum concentration was measured with a validated immunoassay during pregnancy at three time points (12-14, 18-20 and 26-28 weeks of gestation). The concentration increased 2.5-fold (P < 0.0001) and 1.7-fold (P = 0.0019) during the follow-up period for control women and women who later developed preeclampsia (PE), respectively, and a significant difference was observed at weeks 26-28 (P = 0.0266). HSD17B1 concentration at all the three time points positively correlated with serum PAPPA measured at the first time point (first time point r = 0.38, P = 1.1 × 10-10; second time point r = 0.27, P = 5.9 × 10-6 and third timepoint r = 0.26, P = 2.3 × 10-5). No correlation was observed between HSD17B1 and placental growth factor (PLGF). Serum HSD17B1 negatively correlated with the mother's weight and body mass index (BMI), mirroring the pattern observed for PAPPA. The univariable logistic regression identified a weak association between HSD17B1 at 26-28 weeks and later development of PE (P = 0.04). The best multivariable model obtained using penalized logistic regression with stable iterative variable selection at 26-28 weeks included HSD17B1, together with PLGF, PAPPA and mother's BMI. While the area under the receiver operating characteristic curve of the model was higher than that of the adjusted PLGF, the difference was not statistically significant. In summary, the serum concentration of HSD17B1 correlated with PAPPA, another protein expressed in syncytiotrophoblasts, and with mother's weight and BMI but could not be considered as an independent marker for PE., (© The Author(s) 2024. Published by Oxford University Press on behalf of Society for the Study of Reproduction. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

We studied the associations between inflammation-related proteins in circulation and complications after pediatric allogenic hematopoietic stem cell transplantation (HSCT), to reveal proteomic signatures or individual soluble proteins associated with specific complications after HSCT. We used a proteomics method called Proximity Extension Assay to repeatedly measure 180 different proteins together with clinical variables, cellular immune reconstitution and blood viral copy numbers in 27 children (1-18 years of age) during a 2-year follow-up after allogenic HSCT. Protein profile analysis was performed using unsupervised hierarchical clustering and a regression-based method, while the Bonferroni-corrected Mann-Whitney U-test was used for time point-specific comparison of individual proteins against outcome. At 6 months after allogenic HSCT, we could identify a protein profile pattern associated with occurrence of the complications such as chronic graft-versus-host disease, viral infections, relapse and death. When protein markers were analyzed separately, the plasma concentration of the inhibitory and cytotoxic T-cell surface protein FCRL6 (Fc receptor-like 6) was higher in patients with cytomegalovirus (CMV) viremia [log 2 -fold change 1.5 (P = 0.00099), 2.5 (P = 0.00035) and 2.2 (P = 0.045) at time points 6, 12 and 24 months]. Flow cytometry confirmed that FCRL6 expression was higher in innate-like γδ T cells, indicating that these cells are involved in controlling CMV reactivation in HSCT recipients. In conclusion, the potentially druggable FCRL6 receptor on cytotoxic T cells appears to have a role in controlling CMV viremia after HSCT. Furthermore, our results suggest that system-level analysis is a useful addition to the studying of single biomarkers in allogenic HSCT., (© 2024 The Authors. Immunology & Cell Biology published by John Wiley & Sons Australia, Ltd on behalf of the Australian and New Zealand Society for Immunology, Inc.)

Aims: Heterogeneity 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 baseline multi-omics data obtained after the diagnosis of type 1 diabetes may provide mechanistic insight into the diverse rates of disease progression after type 1 diabetes diagnosis., Methods: We 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., Results: Two molecular signatures were significantly correlated with fasting C-peptide levels. One signature showed a correlation to neutrophil degranulation, cytokine signalling, lymphoid and non-lymphoid cell interactions and G-protein coupled receptor signalling events that were inversely associated with a rapid decline in β-cell function. The second signature was related to translation and viral infection was 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., Conclusions: Features 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., (© 2024 The Author(s). Diabetes/Metabolism Research and Reviews published by John Wiley & Sons Ltd.)

Gene regulatory elements, such as enhancers, greatly influence cell identity by tuning the transcriptional activity of specific cell types. Dynamics of enhancer landscape during early human Th17 cell differentiation remains incompletely understood. Leveraging ATAC-seq-based profiling of chromatin accessibility and comprehensive analysis of key histone marks, we identified a repertoire of enhancers that potentially exert control over the fate specification of Th17 cells. We found 23 SNPs associated with autoimmune diseases within Th17-enhancers that precisely overlapped with the binding sites of transcription factors actively engaged in T-cell functions. Among the Th17-specific enhancers, we identified an enhancer in the intron of RORA and demonstrated that this enhancer positively regulates RORA transcription. Moreover, CRISPR-Cas9-mediated deletion of a transcription factor binding site-rich region within the identified RORA enhancer confirmed its role in regulating RORA transcription. These findings provide insights into the potential mechanism by which the RORA enhancer orchestrates Th17 differentiation., Competing Interests: Declaration of competing interest Alexander Marson is a co-founder of Arsenal Biosciences, Spotlight Therapeutics, and Survey Genomics, serves on the boards of directors at Spotlight Therapeutics and Survey Genomics, is a board observer (and former member of the board of directors) at Arsenal Biosciences, is a member of the scientific advisory boards of Arsenal Biosciences, Spotlight Therapeutics, Survey Genomics, NewLimit, Amgen, Tenaya, and Lightcast, owns stock in Arsenal Biosciences, Spotlight Therapeutics, NewLimit, Survey Genomics, PACT Pharma, Tenaya, and Lightcast, and has received fees from Arsenal Biosciences, Spotlight Therapeutics, NewLimit, 23andMe, PACT Pharma, Juno Therapeutics, Tenaya, Lightcast, Trizell, Vertex, Merck, Amgen, Genentech, AlphaSights, Rupert Case Management, Bernstein, and ALDA. A.M. is an investor in and informal advisor to Offline Ventures and a client of EPIQ. The Marson laboratory has received research support from Juno Therapeutics, Epinomics, Sanofi, GlaxoSmithKline, Gilead, and Anthem. All other authors declare no competing interests. The ChIP-seq and ATAC-seq raw and processed data has been submitted to GEO and can be accessed with the following super-series accession number: GSE243064., (Copyright © 2023. Published by Elsevier Inc.)

Regulatory T cells (Tregs) are central in controlling immune responses, and dysregulation of their function can lead to autoimmune disorders or cancer. Despite extensive studies on Tregs, the basis of epigenetic regulation of human Treg development and function is incompletely understood. Long intergenic noncoding RNAs (lincRNA)s are important for shaping and maintaining the epigenetic landscape in different cell types. In this study, we identified a gene on the chromosome 6p25.3 locus, encoding a lincRNA, that was up-regulated during early differentiation of human Tregs. The lincRNA regulated the expression of interleukin-2 receptor alpha (IL2RA), and we named it the lincRNA regulator of IL2RA (LIRIL2R). Through transcriptomics, epigenomics, and proteomics analysis of LIRIL2R-deficient Tregs, coupled with global profiling of LIRIL2R binding sites using chromatin isolation by RNA purification, followed by sequencing, we identified IL2RA as a target of LIRIL2R. This nuclear lincRNA binds upstream of the IL2RA locus and regulates its epigenetic landscape and transcription. CRISPR-mediated deletion of the LIRIL2R-bound region at the IL2RA locus resulted in reduced IL2RA expression. Notably, LIRIL2R deficiency led to reduced expression of Treg-signature genes (e.g., FOXP3 , CTLA4 , and PDCD1 ), upregulation of genes associated with effector T cells (e.g., SATB1 and GATA3 ), and loss of Treg-mediated suppression., Competing Interests: Competing interests statement:A.M. is a cofounder of Arsenal Biosciences, Function Bio, Spotlight Therapeutics, and Survey Genomics; serves on the boards of directors at Function Bio, Spotlight Therapeutics, and Survey Genomics; is a member of the scientific advisory boards of Arsenal Biosciences, Function Bio, Spotlight Therapeutics, Survey Genomics, NewLimit, Amgen, Tenaya, and Lightcast; owns stock in Arsenal Biosciences, Function Bio, Spotlight Therapeutics, NewLimit, Survey Genomics, Tenaya, and Lightcast; and has received fees from Arsenal Biosciences, Spotlight Therapeutics, NewLimit, Amgen, 23andMe, PACT Pharma, Juno Therapeutics, Tenaya, Survey Genomics, Lightcast, Gilead, Trizell, Vertex, Merck, Genentech, AlphaSights, Rupert Case Management, Bernstein, GLG, ClearView Healthcare Partners, and ALDA. A.M. is an investor in and informal advisor to Offline Ventures and a client of EPIQ. S.B.A.A., U.U.K., S.P., V.K., O.R., and R.L. are inventors in a European patent (Oligonucleotides for Modulating Regulatory T Cell Mediated Immunosuppression, patent number: WO2023242469A1) related to this manuscript. All other authors declare no competing interests.

In-utero and dietary factors make important contributions toward health and development in early childhood. In this respect, serum proteomics of maturing infants can provide insights into studies of childhood diseases, which together with perinatal proteomes could reveal further biological perspectives. Accordingly, to determine differences between feeding groups and changes in infancy, serum proteomics analyses of mother-infant dyads with HLA-conferred susceptibility to type 1 diabetes ( n  = 22), weaned to either an extensively hydrolyzed or regular cow's milk formula, were made. The LC-MS/MS analyses included samples from the beginning of third trimester, the time of delivery, 3 months postpartum, cord blood, and samples from the infants at 3, 6, 9, and 12 months. Correlations between ranked protein intensities were detected within the dyads, together with perinatal and age-related changes. Comparison with intestinal permeability data revealed a number of significant correlations, which could merit further consideration in this context., Competing Interests: The authors declare no competing interests., (© 2024 The Author(s).)

Previous studies have revealed heterogeneity in the progression to clinical type 1 diabetes in children who develop islet-specific antibodies either to insulin (IAA) or glutamic acid decarboxylase (GADA) as the first autoantibodies. Here, we test the hypothesis that children who later develop clinical disease have different early immune responses, depending on the type of the first autoantibody to appear (GADA-first or IAA-first). We use mass cytometry for deep immune profiling of peripheral blood mononuclear cell samples longitudinally collected from children who later progressed to clinical disease (IAA-first, GADA-first, ≥2 autoantibodies first groups) and matched for age, sex, and HLA controls who did not, as part of the Type 1 Diabetes Prediction and Prevention study. We identify differences in immune cell composition of children who later develop disease depending on the type of autoantibodies that appear first. Notably, we observe an increase in CD161 expression in natural killer cells of children with ≥2 autoantibodies and validate this in an independent cohort. The results highlight the importance of endotype-specific analyses and are likely to contribute to our understanding of pathogenic mechanisms underlying type 1 diabetes development., (© 2024. The Author(s).)

Objectives: Although deep learning has demonstrated substantial potential in automatic quantification of joint damage in rheumatoid arthritis (RA), evidence for detecting longitudinal changes at an individual patient level is lacking. Here, we introduce and externally validate our automated RA scoring algorithm (AuRA), and demonstrate its utility for monitoring radiographic progression in a real-world setting., Methods: The algorithm, originally developed during the Rheumatoid Arthritis 2-Dialogue for Reverse Engineering Assessment and Methods (RA2-DREAM) challenge, was trained to predict expert-curated Sharp-van der Heijde total scores in hand and foot radiographs from two previous clinical studies (n = 367). We externally validated AuRA against data (n = 205) from Turku University Hospital and compared the performance against two top-performing RA2-DREAM solutions. Finally, for 54 patients, we extracted additional radiograph sets from another control visit to the clinic (average time interval of 4.6 years)., Results: In the external validation cohort, with a root-mean-square-error (RMSE) of 23.6, AuRA outperformed both top-performing RA2-DREAM algorithms (RMSEs 35.0 and 35.6). The improved performance was explained mostly by lower errors at higher expert-assessed scores. The longitudinal changes predicted by our algorithm were significantly correlated with changes in expert-assessed scores (Pearson's R = 0.74, p< 0.001)., Conclusion: AuRA had the best external validation performance and demonstrated potential for detecting longitudinal changes in joint damage. Available in https://hub.docker.com/r/elolab/aura, our algorithm can easily be applied for automatic detection of radiographic progression in the future, reducing the need for laborious manual scoring., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Society for Rheumatology.)

Background: Although prostate cancer (PCa) is the most common cancer in men in Western countries, there is significant variability in geographical incidence. This might result from genetic factors, discrepancies in screening policies, or differences in lifestyle. Gut microbiota has recently been associated with cancer progression, but its role in PCa is unclear., Objective: Characterization of the gut microbiota and its functions associated with PCa., Design Setting and Participants: In a prospective multicenter clinical trial (NCT02241122), the gut microbiota profiles of 181 men with a clinical suspicion of PCa were assessed utilizing 16S rRNA sequencing., Outcome Measurements and Statistical Analysis: Sequences were assigned to operational taxonomic units, differential abundance analysis, and α- and β-diversities, and predictive functional analyses were performed. Plasma steroid hormone levels corresponding to the predicted microbiota steroid hormone biosynthesis profiles were investigated., Results and Limitations: Of 364 patients, 181 were analyzed, 60% of whom were diagnosed with PCa. Microbiota composition and diversity were significantly different in PCa, partially affected by Prevotella 9 , the most abundant genus of the cohort, and significantly higher in PCa patients. Predictive functional analyses revealed higher 5-α-reductase, copper absorption, and retinol metabolism in the PCa-associated microbiome. Plasma testosterone was associated negatively with the predicted microbial 5-α-reductase level., Conclusions: Gut microbiota of the PCa patients differed significantly compared with benign individuals. Microbial 5-α-reductase, copper absorption, and retinol metabolism are potential mechanisms of action. These findings support the observed association of lifestyle, geography, and PCa incidence., Patient Summary: In this report, we found that several microbes and potential functions of the gut microbiota are altered in prostate cancer compared with benign cases. These findings suggest that gut microbiota could be the link between environmental factors and prostate cancer., (© 2024 The Author(s).)

Single-cell transcriptomics allows unbiased characterization of cell heterogeneity in a sample by profiling gene expression at single-cell level. These profiles capture snapshots of transient or steady states in dynamic processes, such as cell cycle, activation, or differentiation, which can be computationally ordered into a "flip-book" of cell development using trajectory inference methods. However, prediction of more complex topology structures, such as multifurcations or trees, remains challenging. In this chapter, we present two user-friendly protocols for inferring tree-shaped single-cell trajectories and pseudotime from single-cell transcriptomics data with Totem. Totem is a trajectory inference method that offers flexibility in inferring both nonlinear and linear trajectories and usability by avoiding the cumbersome fine-tuning of parameters. The QuickStart protocol provides a simple and practical example, whereas the GuidedStart protocol details the analysis step-by-step. Both protocols are demonstrated using a case study of human bone marrow CD34+ cells, allowing the study of the branching of three lineages: erythroid, lymphoid, and myeloid. All the analyses can be fully reproduced in Linux, macOS, and Windows operating systems (amd64 architecture) with >8 Gb of RAM using the provided docker image distributed with notebooks, scripts, and data in Docker Hub (elolab/repro-totem-ti). These materials are shared online under open-source license at https://elolab.github.io/Totem-protocol ., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

The serine/threonine-specific Moloney murine leukemia virus (PIM) kinase family (i.e., PIM1, PIM2, and PIM3) has been extensively studied in tumorigenesis. PIM kinases are downstream of several cytokine signaling pathways that drive immune-mediated diseases. Uncontrolled T helper 17 (Th17) cell activation has been associated with the pathogenesis of autoimmunity. However, the detailed molecular function of PIMs in human Th17 cell regulation has yet to be studied. In the present study, we comprehensively investigated how the three PIMs simultaneously alter transcriptional gene regulation during early human Th17 cell differentiation. By combining PIM triple knockdown with bulk and scRNA-seq approaches, we found that PIM deficiency promotes the early expression of key Th17-related genes while suppressing Th1-lineage genes. Further, PIMs modulate Th cell signaling, potentially via STAT1 and STAT3. Overall, our study highlights the inhibitory role of PIMs in human Th17 cell differentiation, thereby suggesting their association with autoimmune phenotypes., Competing Interests: Declaration of interests A.M. is a co-founder of Arsenal Biosciences, Spotlight Therapeutics, and Survey Genomics; serves on the boards of directors at Spotlight Therapeutics and Survey Genomics; is a board observer (and former member of the board of directors) at Arsenal Biosciences; is a member of the scientific advisory boards of Arsenal Biosciences, Spotlight Therapeutics, Survey Genomics, NewLimit, Amgen, and Tenaya; owns stock in Arsenal Biosciences, Spotlight Therapeutics, NewLimit, Survey Genomics, PACT Pharma, and Tenaya; and has received fees from Arsenal Biosciences, Spotlight Therapeutics, NewLimit, 23andMe, PACT Pharma, Juno Therapeutics, Trizell, Vertex, Merck, Amgen, Genentech, AlphaSights, Rupert Case Management, Bernstein, and ALDA. A.M. is an investor in and informal advisor to Offline Ventures and a client of EPIQ. The Marson laboratory has received research support from Juno Therapeutics, Epinomics, Sanofi, GlaxoSmithKline, Gilead, and Anthem., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

This study aims to develop and validate a modeling framework to predict long-term weight change on the basis of self-reported weight data. The aim is to enable focusing resources of health systems on individuals that are at risk of not achieving their goals in weight loss interventions, which would help both health professionals and the individuals in weight loss management. The weight loss prediction models were built on 327 participants, aged 21-78, from a Finnish weight coaching cohort, with at least 9 months of self-reported follow-up weight data during weight loss intervention. With these data, we used six machine learning methods to predict weight loss after 9 months and selected the best performing models for implementation as modeling framework. We trained the models to predict either three classes of weight change (weight loss, insufficient weight loss, weight gain) or five classes (high/moderate/insufficient weight loss, high/low weight gain). Finally, the prediction accuracy was validated with an independent cohort of overweight UK adults (n = 184). Of the six tested modeling approaches, logistic regression performed the best. Most three-class prediction models achieved prediction accuracy of > 50% already with half a month of data and up to 97% with 8 months. The five-class prediction models achieved accuracies from 39% (0.5 months) to 89% (8 months). Our approach provides an accurate prediction method for long-term weight loss, with potential for easier and more efficient management of weight loss interventions in the future. A web application is available: https://elolab.shinyapps.io/WeightChangePredictor/ .The trial is registered at clinicaltrials.gov/ct2/show/NCT04019249 (Clinical Trials Identifier NCT04019249), first posted on 15/07/2019., (© 2023. The Author(s).)

Aims/hypothesis: There is a growing need for markers that could help indicate the decline in beta cell function and recognise the need and efficacy of intervention in type 1 diabetes. Measurements of suitably selected serum markers could potentially provide a non-invasive and easily applicable solution to this challenge. Accordingly, we evaluated a broad panel of proteins previously associated with type 1 diabetes in serum from newly diagnosed individuals during the first year from diagnosis. To uncover associations with beta cell function, comparisons were made between these targeted proteomics measurements and changes in fasting C-peptide levels. To further distinguish proteins linked with the disease status, comparisons were made with measurements of the protein targets in age- and sex-matched autoantibody-negative unaffected family members (UFMs)., Methods: Selected reaction monitoring (SRM) mass spectrometry analyses of serum, targeting 85 type 1 diabetes-associated proteins, were made. Sera from individuals diagnosed under 18 years (n=86) were drawn within 6 weeks of diagnosis and at 3, 6 and 12 months afterwards (288 samples in total). The SRM data were compared with fasting C-peptide/glucose data, which was interpreted as a measure of beta cell function. The protein data were further compared with cross-sectional SRM measurements from UFMs (n=194)., Results: Eleven proteins had statistically significant associations with fasting C-peptide/glucose. Of these, apolipoprotein L1 and glutathione peroxidase 3 (GPX3) displayed the strongest positive and inverse associations, respectively. Changes in GPX3 levels during the first year after diagnosis indicated future fasting C-peptide/glucose levels. In addition, differences in the levels of 13 proteins were observed between the individuals with type 1 diabetes and the matched UFMs. These included GPX3, transthyretin, prothrombin, apolipoprotein C1 and members of the IGF family., Conclusions/interpretation: The association of several targeted proteins with fasting C-peptide/glucose levels in the first year after diagnosis suggests their connection with the underlying changes accompanying alterations in beta cell function in type 1 diabetes. Moreover, the direction of change in GPX3 during the first year was indicative of subsequent fasting C-peptide/glucose levels, and supports further investigation of this and other serum protein measurements in future studies of beta cell function in type 1 diabetes., (© 2023. The Author(s).)

Motivation: 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., Results: 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., Availability and Implementation: Totem is available as an R package at https://github.com/elolab/Totem., (© The Author(s) 2023. Published by Oxford University Press.)

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 bases of variants, wildtype/mutated DNA sequences, variant nomenclatures, distances between adjacent variants, tandem repeat regions, and custom annotation with user customizable options. Our analyses demonstrate that VarSCAT is more versatile and customizable than the currently available methods or strategies for annotating variants in short tandem repeat (STR) regions or insertions and deletions (indels) with breakpoint ambiguity. 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 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 from https://github.com/elolab/VarSCAT., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Frequent laboratory monitoring is recommended for early identification of toxicity when initiating conventional synthetic disease-modifying antirheumatic drugs (csDMARDs). We aimed at developing a risk prediction model to individualize laboratory testing at csDMARD initiation. We identified inflammatory joint disease patients (N = 1196) initiating a csDMARD in Turku University Hospital 2013-2019. Baseline and follow-up safety monitoring results were drawn from electronic health records. For rheumatoid arthritis patients, diagnoses and csDMARD initiation/cessation dates were manually confirmed. Primary endpoint was alanine transaminase (ALT) elevation of more than twice the upper limit of normal (ULN) within 6 months after treatment initiation. Computational models for predicting incident ALT elevations were developed using Lasso Cox proportional hazards regression with stable iterative variable selection (SIVS) and were internally validated against a randomly selected test cohort (1/3 of the data) that was not used for training the models. Primary endpoint was reached in 82 patients (6.9%). Among baseline variables, Lasso model with SIVS predicted subsequent ALT elevations of > 2 × ULN using higher ALT, csDMARD other than methotrexate or sulfasalazine and psoriatic arthritis diagnosis as important predictors, with a concordance index of 0.71 in the test cohort. Respectively, at first follow-up, in addition to baseline ALT and psoriatic arthritis diagnosis, also ALT change from baseline was identified as an important predictor resulting in a test concordance index of 0.72. Our computational model predicts ALT elevations after the first follow-up test with good accuracy and can help in optimizing individual testing frequency., (© 2023. Springer Nature Limited.)

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., Competing Interests: Declaration of interests CM serves or has served on the advisory panel for ActoBio Therapeutics, AstraZeneca, Avotres, Boehringer Ingelheim, Eli Lilly and Company, Imcyse, Insulet, Mannkind, Medtronic, Merck Sharp and Dohme Ltd., Novartis, Novo Nordisk, Pfizer, Roche, Sandoz, Sanofi, Vertex, and Zealand Pharma. CM serves or has served on the speakers bureau for AstraZeneca, Boehringer Ingelheim, Eli Lilly and Company, Novartis, Novo Nordisk, and Sanofi. “T.G. was supported by Academy of Finland, Tampere University and University of Turku”., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

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.

Background: The use of glucocorticoids has given contradictory results for treating acute respiratory distress syndrome (ARDS). The use of intravenous Interferon beta (IFN β) for the treatment of ARDS was recently tested in a phase III ARDS trial (INTEREST), in which more than half of the patients simultaneously received glucocorticoids. Trial results showed deleterious effects of glucocorticoids when administered together with IFN β, and therefore, we aimed at finding the reason behind this., Methods: We first sequenced the genes encoding the IFN α/β receptor of the patients, who participated in the INTEREST study (ClinicalTrials.gov Identifier:  NCT02622724 , November 24, 2015) in which the patients were randomized to receive an intravenous injection of IFN β-1a (144 patients) or placebo (152 patients). Genetic background was analyzed against clinical outcome, concomitant medication, and pro-inflammatory cytokine levels. Thereafter, we tested the influence of the genetic background on IFN α/β receptor expression in lung organ cultures and whether, it has any effect on transcription factors STAT1 and STAT2 involved in IFN signaling., Results: We found a novel disease association of a SNP rs9984273, which is situated in the interferon α/β receptor subunit 2 (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, more rapid decrease of IFN γ and interleukin-6 (IL-6) levels and better outcome in IFN β treated patients with ARDS, while the major allele associates with a poor outcome especially under concomitant IFN β and glucocorticoid treatment. Moreover, the minor allele of rs9984273 associates with a less severe form of coronavirus diseases (COVID-19) according to the COVID-19 Host Genetics Initiative database., Conclusions: 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 signaling and glucocorticoids., (© 2023. The Author(s).)

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., (© 2023. The Author(s).)

Background: In coeliac disease (CoD), the role of B-cells has mainly been considered to be production of antibodies. The functional role of B-cells has not been analysed extensively in CoD., Methods: We conducted a study to characterize gene expression in B-cells from children developing CoD early in life using samples collected before and at the diagnosis of the disease. Blood samples were collected from children at risk at 12, 18, 24 and 36 months of age. RNA from peripheral blood CD19 + cells was sequenced and differential gene expression was analysed using R package Limma., Findings: Overall, we found one gene, HNRNPL , modestly downregulated in all patients (logFC -0·7; q = 0·09), and several others downregulated in those diagnosed with CoD already by the age of 2 years., Interpretation: The data highlight the role of B-cells in CoD development. The role of HNRPL in suppressing enteroviral replication suggests that the predisposing factor for both CoD and enteroviral infections is the low level of HNRNPL expression., Funding: EU FP7 grant no. 202063, EU Regional Developmental Fund and research grant PRG712, The Academy of Finland Centre of Excellence in Molecular Systems Immunology and Physiology Research (SyMMyS) 2012-2017, grant no. 250114) and, AoF Personalized Medicine Program (grant no. 292482), AoF grants 292335, 294337, 319280, 31444, 319280, 329277, 331790) and grants from the Sigrid Jusélius Foundation (SJF)., Competing Interests: The authors declare no competing interests., (© 2023 The Authors. Published by Elsevier Ltd.)

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., (© 2022. The Author(s).)

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., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)

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., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

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.

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 Author(s) 2022. Published by Oxford University Press.)

Aims/hypothesis: Distinct DNA methylation patterns have recently been observed to precede type 1 diabetes in whole blood collected from young children. Our aim was to determine whether perinatal DNA methylation is associated with later progression to type 1 diabetes., Methods: Reduced representation bisulphite sequencing (RRBS) analysis was performed on umbilical cord blood samples collected within the Finnish Type 1 Diabetes Prediction and Prevention (DIPP) Study. Children later diagnosed with type 1 diabetes and/or who tested positive for multiple islet autoantibodies (n = 43) were compared with control individuals (n = 79) who remained autoantibody-negative throughout the DIPP follow-up until 15 years of age. Potential confounding factors related to the pregnancy and the mother were included in the analysis., Results: No differences in the umbilical cord blood methylation patterns were observed between the cases and controls at a false discovery rate <0.05., Conclusions/interpretation: Based on our results, differences between children who progress to type 1 diabetes and those who remain healthy throughout childhood are not yet present in the perinatal DNA methylome. However, we cannot exclude the possibility that such differences would be found in a larger dataset., (© 2022. The Author(s).)

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., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Välikangas, Junttila, Rytkönen, Kukkonen-Macchi, Suomi and Elo.)

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.

T helper 17 (Th17) cells protect against fungal and bacterial infections and are implicated in autoimmunity. Several long intergenic noncoding RNAs (lincRNA) are induced during Th17 differentiation, however, their contribution to Th17 differentiation is poorly understood. We aimed to characterize the function of the lincRNA Myocardial Infarction Associated Transcript (MIAT) during early human Th17 cell differentiation. We found MIAT to be upregulated early after induction of human Th17 cell differentiation along with an increase in the chromatin accessibility at the gene locus. STAT3, a key regulator of Th17 differentiation, directly bound to the MIAT promoter and induced its expression during the early stages of Th17 cell differentiation. MIAT resides in the nucleus and regulates the expression of several key Th17 genes, including IL17A, IL17F, CCR6 and CXCL13, possibly by altering the chromatin accessibility of key loci, including IL17A locus. Further, MIAT regulates the expression of protein kinase C alpha (PKCα), an upstream regulator of IL17A. A reanalysis of published single-cell RNA-seq data showed that MIAT was expressed in T cells from the synovium of RA patients. Our results demonstrate that MIAT contributes to human Th17 differentiation by upregulating several genes implicated in Th17 differentiation. High MIAT expression in T cells of RA patient synovia suggests a possible role of MIAT in Th17 mediated autoimmune pathologies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Khan, Khan, Kalim, Khan, Junttila, Paulin, Kong, Rasool, Elo and Lahesmaa.)

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., (© The Author(s) 2021. Published by Oxford University Press.)