Summary The human gut microbiome matures toward the adult composition during the first years of life and is implicated in early immune development. Here, we investigate the effects of microbial genomic diversity on gut microbiome development using integrated early childhood datasets collected in the DIABIMMUNE study in Finland, Estonia and Russian Karelia. We show that gut microbial diversity is associated with household location and linear growth of children. Single nucleotide polymorphism (SNP)- and metagenomic assembly-based strain tracking revealed large and highly dynamic microbial pangenomes, especially in the genus Bacteroides , in which we identified evidence of variability deriving from Bacteroides -targeting bacteriophages. Our analyses revealed functional consequences of strain diversity; only 10% of Finnish infants harbored Bifidobacterium longum subsp. infantis , a subspecies specialized in human milk metabolism, whereas Russian infants commonly maintained a probiotic Bifidobacterium bifidum strain in infancy. Groups of bacteria contributing to diverse, characterized metabolic pathways converged to highly subject-specific configurations over the first two years of life. This longitudinal study extends the current view of early gut microbial community assembly based on strain-level genomic variation.

The appearance of type 1 diabetes (T1D)-associated autoantibodies is the first and only measurable parameter to predict progression toward T1D in genetically susceptible individuals. However, autoantibodies indicate an active autoimmune reaction, wherein the immune tolerance is already broken. Therefore, there is a clear and urgent need for new biomarkers that predict the onset of the autoimmune reaction preceding autoantibody positivity or reflect progressive β-cell destruction. Here we report the mRNA sequencing–based analysis of 306 samples including fractionated samples of CD4+ and CD8+ T cells as well as CD4−CD8− cell fractions and unfractionated peripheral blood mononuclear cell samples longitudinally collected from seven children who developed β-cell autoimmunity (case subjects) at a young age and matched control subjects. We identified transcripts, including interleukin 32 (IL32), that were upregulated before T1D-associated autoantibodies appeared. Single-cell RNA sequencing studies revealed that high IL32 in case samples was contributed mainly by activated T cells and NK cells. Further, we showed that IL32 expression can be induced by a virus and cytokines in pancreatic islets and β-cells, respectively. The results provide a basis for early detection of aberrations in the immune system function before T1D and suggest a potential role for IL32 in the pathogenesis of T1D.

Background Immunoglobulin G4-related disease (IgG4-RD) and systemic sclerosis (SSc) are rare autoimmune diseases characterized by the presence of CD4+ cytotoxic T cells in the blood as well as inflammation and fibrosis in various organs, but they have no established etiologies. Similar to other autoimmune diseases, the gut microbiome might encode disease-triggering or disease-sustaining factors. Methods The gut microbiomes from IgG4-RD and SSc patients as well as healthy individuals with no recent antibiotic treatment were studied by metagenomic sequencing of stool DNA. De novo assembly-based taxonomic and functional characterization, followed by association and accessory gene set enrichment analysis, were applied to describe microbiome changes associated with both diseases. Results Microbiomes of IgG4-RD and SSc patients distinctly separated from those of healthy controls: numerous opportunistic pathogenic Clostridium and typically oral Streptococcus species were significantly overabundant, while Alistipes, Bacteroides, and butyrate-producing species were depleted in the two diseases compared to healthy controls. Accessory gene content analysis in these species revealed an enrichment of Th17-activating Eggerthella lenta strains in IgG4-RD and SSc and a preferential colonization of a homocysteine-producing strain of Clostridium bolteae in SSc. Overabundance of the classical mevalonate pathway, hydroxyproline dehydratase, and fibronectin-binding protein in disease microbiomes reflects potential functional differences in host immune recognition and extracellular matrix utilization associated with fibrosis. Strikingly, the majority of species that were differentially abundant in IgG4-RD and SSc compared to controls showed the same directionality in both diseases. Compared with multiple sclerosis and rheumatoid arthritis, the gut microbiomes of IgG4-RD and SSc showed similar signatures; in contrast, the most differentially abundant taxa were not the facultative anaerobes consistently identified in inflammatory bowel diseases, suggesting the microbial signatures of IgG4-RD and SSc do not result from mucosal inflammation and decreased anaerobism. Conclusions These results provide an initial characterization of gut microbiome ecology in fibrosis-prone IgG4-RD and SSc and reveal microbial functions that offer insights into the pathophysiology of these rare diseases.

In the version of this Article originally published, in the first sentence of the second paragraph of the Discussion section, the word "operingrationally" should have read "operationally". This has now been amended in all versions of the Article.

Numerous time-course gene expression datasets have been generated for studying the biological dynamics that drive disease progression; and nearly as many methods have been proposed to analyse them. However, barely any method exists that can appropriately model time-course data while accounting for heterogeneity that entails many complex diseases. Most methods manage to fulfil either one of those qualities, but not both. The lack of appropriate methods hinders our capability of understanding the disease process and pursuing preventive treatments. We present a method that models time-course data in a personalised manner using Gaussian processes in order to identify differentially expressed genes (DEGs); and combines the DEG lists on a pathway-level using a permutation-based empirical hypothesis testing in order to overcome gene-level variability and inconsistencies prevalent to datasets from heterogenous diseases. Our method can be applied to study the time-course dynamics, as well as specific time-windows of heterogeneous diseases. We apply our personalised approach on three longitudinal type 1 diabetes (T1D) datasets, where the first two are used to determine perturbations taking place during early prognosis of the disease, as well as in time-windows before autoantibody positivity and T1D diagnosis; and the third is used to assess the generalisability of our method. By comparing to non-personalised methods, we demonstrate that our approach is biologically motivated and can reveal more insights into progression of heterogeneous diseases. With its robust capabilities of identifying disease-relevant pathways, our approach could be useful for predicting events in the progression of heterogeneous diseases and even for biomarker identification.

Numerous time-course gene expression datasets have been curated for studying the biological dynamics that drive disease progression; and nearly as many methods have been proposed to analyse them. However, barely any method exists that can appropriately model time-course data and at the same time account for heterogeneity that entails many complex diseases. Most methods manage to fulfil either one of those qualities, but not both. The lack of appropriate methods hinders our capability of understanding the disease process and pursuing preventive or curative treatments. Here, we present a method that models time-course data in a personalised manner, i.e. for each case-control pair individually, using Gaussian processes in order to identify differentially expressed genes (DEGs); and combines the lists of DEGs on a pathway-level using a permutation-based empirical hypothesis testing in order to overcome gene-level variability and inconsistencies prevalent to heterogeneous datasets from complex diseases. Our method can be applied to study the time-course dynamics as well as specific time-windows of heterogeneous diseases. We apply our personalised approach on two longitudinal type 1 diabetes (T1D) datasets to determine perturbations that take place during early prognosis of the disease as well as in time-windows before seroconversion and clinical onset of T1D. By comparing to non-personalised methods, we demonstrate that our approach is biologically motivated and can reveal more insights into progression of heterogeneous diseases. With its robust capabilities of identifying immunologically interesting and disease-relevant pathways, our approach could be useful for predicting certain events in the progression of heterogeneous diseases and even biomarker identification.Availability: The implemented code of our personalised approach will be available online upon publication. * Co-first author; contributed equally to this work