Diffusion Restrictions Surrounding Mitochondria: A Mathematical Model of Heart Muscle Fibers

Several experiments on permeabilized heart muscle fibers suggest the existence of diffusion restrictions grouping mitochondria and surrounding ATPases. The specific causes of these restrictions are not known, but intracellular structures are speculated to act as diffusion barriers. In this work, we assume that diffusion restrictions are induced by sarcoplasmic reticulum (SR), cytoskeleton proteins localized near SR, and crowding of cytosolic proteins. The aim of this work was to test whether such localization of diffusion restrictions would be consistent with the available experimental data and evaluate the extent of the restrictions. For that, a three-dimensional finite-element model was composed with the geometry based on mitochondrial and SR structural organization. Diffusion restrictions induced by SR and cytoskeleton proteins were varied with other model parameters to fit the set of experimental data obtained on permeabilized rat heart muscle fibers. There are many sets of model parameters that were able to reproduce all experiments considered in this work. However, in all the sets, <5–6% of the surface formed by SR and associated cytoskeleton proteins is permeable to metabolites. Such a low level of permeability indicates that the proteins should play a dominant part in formation of the diffusion restrictions

The Consensus Molecular Subtypes of Colorectal Cancer

Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use -- https://www.nature.com/authors/policies/license.html#termsColorectal cancer (CRC) is a frequently lethal disease with heterogeneous outcomes and drug responses. To resolve inconsistencies among the reported gene expression-based CRC classifications and facilitate clinical translation, we formed an international consortium dedicated to large-scale data sharing and analytics across expert groups. We show marked interconnectivity between six independent classification systems coalescing into four consensus molecular subtypes (CMS) with distinguishing features: CMS1 (MSI Immune, 14%), hypermutated, microsatellite unstable, strong immune activation; CMS2 (Canonical, 37%), epithelial, chromosomally unstable, marked WNT and MYC signaling activation; CMS3 (Metabolic, 13%), epithelial, evident metabolic dysregulation; and CMS4 (Mesenchymal, 23%), prominent transforming growth factor β activation, stromal invasion, and angiogenesis. Samples with mixed features (13%) possibly represent a transition phenotype or intra-tumoral heterogeneity. We consider the CMS groups the most robust classification system currently available for CRC - with clear biological interpretability - and the basis for future clinical stratification and subtype-based targeted interventions

Weighted Gene Co-Expression Network Analysis Identifies a Functional Guild and Metabolite Cluster Mediating the Relationship between Mucosal Inflammation and Adherence to the Mediterranean Diet in Ulcerative Colitis

Diet influences the pathogenesis and clinical course of inflammatory bowel disease (IBD). The Mediterranean diet (MD) is linked to reductions in inflammatory biomarkers and alterations in microbial taxa and metabolites associated with health. We aimed to identify features of the gut microbiome that mediate the relationship between the MD and fecal calprotectin (FCP) in ulcerative colitis (UC). Weighted gene co-expression network analysis (WGCNA) was used to identify modules of co-abundant microbial taxa and metabolites correlated with the MD and FCP. The features considered were gut microbial taxa, serum metabolites, dietary components, short-chain fatty acid and bile acid profiles in participants that experienced an increase (n = 13) or decrease in FCP (n = 16) over eight weeks. WGCNA revealed ten modules containing sixteen key features that acted as key mediators between the MD and FCP. Three taxa (Faecalibacterium prausnitzii, Dorea longicatena, Roseburia inulinivorans) and a cluster of four metabolites (benzyl alcohol, 3-hydroxyphenylacetate, 3-4-hydroxyphenylacetate and phenylacetate) demonstrated a strong mediating effect (ACME: −1.23, p = 0.004). This study identified a novel association between diet, inflammation and the gut microbiome, providing new insights into the underlying mechanisms of how a MD may influence IBD. See clinicaltrials.gov (NCT04474561)

Divergent maturational patterns of the infant bacterial and fungal gut microbiome in the first year of life are associated with inter-kingdom community dynamics and infant nutrition

Abstract Background The gut microbiome undergoes primary ecological succession over the course of early life before achieving ecosystem stability around 3 years of age. These maturational patterns have been well-characterized for bacteria, but limited descriptions exist for other microbiota members, such as fungi. Further, our current understanding of the prevalence of different patterns of bacterial and fungal microbiome maturation and how inter-kingdom dynamics influence early-life microbiome establishment is limited. Results We examined individual shifts in bacterial and fungal alpha diversity from 3 to 12 months of age in 100 infants from the CHILD Cohort Study. We identified divergent patterns of gut bacterial or fungal microbiome maturation in over 40% of infants, which were characterized by differences in community composition, inter-kingdom dynamics, and microbe-derived metabolites in urine, suggestive of alterations in the timing of ecosystem transitions. Known microbiome-modifying factors, such as formula feeding and delivery by C-section, were associated with atypical bacterial, but not fungal, microbiome maturation patterns. Instead, fungal microbiome maturation was influenced by prenatal exposure to artificially sweetened beverages and the bacterial microbiome, emphasizing the importance of inter-kingdom dynamics in early-life colonization patterns. Conclusions These findings highlight the ecological and environmental factors underlying atypical patterns of microbiome maturation in infants, and the need to incorporate multi-kingdom and individual-level perspectives in microbiome research to improve our understandings of gut microbiome maturation patterns in early life and how they relate to host health. Video Abstrac

Additional file 1 of Divergent maturational patterns of the infant bacterial and fungal gut microbiome in the first year of life are associated with inter-kingdom community dynamics and infant nutrition

Additional file 1: Table S1. 16S and ITS2 read counts before and after sequence processing with the DADA2 pipeline (related to Figure S1). Table S2. Differences in CLR-transformed abundance of the top 15 bacterial genera by infant age and bacterial alpha diversity trend (related to Fig. 3A-B and Figure S4). Table S3. Differences in CLR-transformed abundance of the top 15 fungal genera by infant age and fungal alpha diversity trend (related to Fig. 3D-E and Figure S5). Table S4. Pair-wise comparison of typical (inverse), bacteria atypical, and fungi atypical inter-kingdom microbial co-occurrence network properties at 3 and 12 months (related to Fig. 4). Table S5. Bacterial co-occurrence network properties between typical and atypical alpha diversity trends at 3 and 12 months (related to Figure S6). Table S6. Fungal co-occurrence network properties between typical and atypical alpha diversity trends at 3 and 12 months (related to Figure S7). Table S7. Inter-kingdom co-occurrence network properties between typical and atypical (bacteria, fungi, or both) alpha diversity trends at 3 and 12 months (related to Figure S8). Table S8. Logistic regression statistics between maternal, infant, and early-life factors and bacterial alpha diversity trend (related to Fig. 4B). Table S9. Logistic regression statistics between maternal, infant, and early-life factors and fungal alpha diversity trend (related to Fig. 4D). Figure S1. 16S and ITS2 sequencing depth and sample composition (related to Table S1). Figure S2. Divergent bacterial richness maturation patterns are observed in the first year of life (related to Fig. 1). Figure S3. Divergent fungal richness maturation patterns are observed in the first year of life (related to Fig. 2). Figure S4. Individual-level taxonomic differences between infants with an increasing vs. decreasing bacterial alpha diversity trend at 3 and 12 months (related to Fig. 3 and Table S2). Figure S5. Individual-level taxonomic differences between infants with a decreasing vs. increasing fungal alpha diversity trend at 3 and 12 months (related to Fig. 3 and Table S3). Figure S6. Differences in bacterial co-occurrence networks are observed between increasing and decreasing alpha diversity trends at 3 and 12 months (related to Fig. 4 and Table S5). Figure S7. Differences in fungal co-occurrence networks are observed between increasing and decreasing alpha diversity trends at 3 and 12 months (related to Fig. 4 and Table S6). Figure S8. Differences in inter-kingdom co-occurrence networks are observed between infants with a typical (inverse) bacterial and fungal alpha diversity trend and atypical changes in bacterial, fungal, or both alpha diversity trends at 3 and 12 months (related to Fig. 4 and Table S7)