A Metabolite-Based Resistance Mechanism Against Malaria.

Whether jaundice, a common presentation of ( .) malaria (1-3) arising from the accumulation of circulating bilirubin, represents an adaptive or maladaptive response to spp. infection is not understood (1-3).

Targeted serum proteomics of longitudinal samples from newly diagnosed youth with type 1 diabetes affirms markers of disease.

Aims/hypothesis: While investigating markers for declining beta cell function in type 1 diabetes, we previously demonstrated 11 statistically significant protein associations with fasting C-peptide/glucose ratios in longitudinal serum samples from newly diagnosed (ND) individuals (n=86; 228 samples in total) participating in the INNODIA (Innovative approaches to understanding and arresting type 1 diabetes) study. Furthermore, comparison with protein measurements from age- and sex-matched autoantibody-negative unaffected family members (UFMs, n=194) revealed differences in the serum levels of 13 target proteins. To further evaluate these findings, we analysed longitudinal serum drawn during the first year after diagnosis from a new group of ND individuals subsequently enrolled in the study, together with samples from additional UFMs.

Metaproteomics Beyond Databases: Addressing the Challenges and Potentials of De Novo Sequencing.

Metaproteomics enables the large-scale characterization of microbial community proteins, offering crucial insights into their taxonomic composition, functional activities, and interactions within their environments. By directly analyzing proteins, metaproteomics offers insights into community phenotypes and the roles individual members play in diverse ecosystems. Although database-dependent search engines are commonly used for peptide identification, they rely on pre-existing protein databases, which can be limiting for complex, poorly characterized microbiomes.

Adaptive sequence alignment for metagenomic data analysis.

With advances in sequencing technologies, the use of high-throughput sequencing to characterize microbial communities is becoming increasingly feasible. However, metagenomic assembly poses computational challenges in reconstructing genes and organisms from complex samples. To address this issue, we introduce a new concept called Adaptive Sequence Alignment (ASA) for analyzing metagenomic DNA sequence data.

Proteomic profiling reveals alterations in metabolic and cellular pathways in severe obesity and following metabolic bariatric surgery.

In this study, we investigated the impact of bariatric surgery on the adipose proteome to better understand the metabolic and cellular mechanisms underlying weight loss following the procedure. A total of 46 patients with severe obesity were included, with samples collected both before and after bariatric surgery. In addition, 15 healthy individuals without obesity who did not undergo surgery served as controls and were studied once.

Profiling steroid hormone landscape of bladder cancer reveals depletion of intratumoural androgens to castration levels: a cross-sectional study.

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.

Phenotypic profiling of human induced regulatory T cells at early differentiation: insights into distinct immunosuppressive potential.

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.

Disruption of HSD17B12 in mouse hepatocytes leads to reduced body weight and defect in the lipid droplet expansion associated with microvesicular steatosis.

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.

Inferring Tree-Shaped Single-Cell Trajectories with Totem.

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.

Prediction of Early Adverse Events After THA: A Comparison of Different Machine-Learning Strategies Based on 262,356 Observations From the Nordic Arthroplasty Register Association (NARA) Dataset.

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.

Multi-omics analysis reveals drivers of loss of β-cell function after newly diagnosed autoimmune type 1 diabetes: An INNODIA multicenter study.

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.

Serum proteomics of mother-infant dyads carrying HLA-conferred type 1 diabetes risk.

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 ( = 22), weaned to either an extensively hydrolyzed or regular cow's milk formula, were made.

Long noncoding RNA LIRIL2R modulates FOXP3 levels and suppressive function of human CD4 regulatory T cells by regulating IL2RA.

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.

A proximal enhancer regulates RORA expression during early human Th17 cell differentiation.

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.

Serum hydroxysteroid (17beta) dehydrogenase 1 concentration in pregnant women correlates with pregnancy-associated plasma protein A but does not serve as an independent marker for preeclampsia†.

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.

Proteomics screening after pediatric allogenic hematopoietic stem cell transplantation reveals an association between increased expression of inhibitory receptor FCRL6 on γδ T cells and cytomegalovirus reactivation.

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.

Distinct cellular immune responses in children en route to type 1 diabetes with different first-appearing autoantibodies.

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.

RUNX transcription factors are essential in maintaining epididymal epithelial differentiation.

Apart from the androgen receptor, transcription factors (TFs) that are required for the development and formation of the different segments of the epididymis have remained unknown. We identified TF families expressed in the developing epididymides, of which many showed segment specificity. From these TFs, down-regulation of runt related transcription factors (RUNXs) 1 and 2 expression coincides with epithelial regression in Dicer1 cKO mice.

Deep learning enables automatic detection of joint damage progression in rheumatoid arthritis-model development and external validation.

Objectives: Although deep learning has demonstrated substantial potential in automatic quantification of joint damage in 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).

PIM kinases regulate early human Th17 cell differentiation.

The serine/threonine-specific Moloney murine leukemia virus (PIM) kinase family (i.e., PIM1, PIM2, and PIM3) has been extensively studied in tumorigenesis.

Development and validation of a weight-loss predictor to assist weight loss management.

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.

Cell-connectivity-guided trajectory inference from single-cell data.

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.