Biology subjects

Diener, C.

Publications and source records attributed to Diener, C..

27 records · Page 2Linked to original sources

bioRxiv2021

Metabolic reprogramming of cytotoxic T lymphocytes by high glucose enhances pancreatic beta cell apoptosis via TRAIL

Cytotoxic T lymphocytes (CTLs) are involved in development of diabetes. However, the impact of excessive glucose on CTL-mediated antigen-independent killing remains elusive. Here, we report that TNF-related apoptosis inducing ligand (TRAIL) is substantially up- regulated in CTLs in environments with high glucose (HG) both in vitro and in vivo. The PI3K- Akt-NF{kappa}B axis and non-mitochondrial reactive oxygen species are essential in HG-induced TRAIL upregulation in CTLs. TRAILhigh CTLs induce apoptosis of pancreatic beta cell line 1.4E7. Metformin and Vitamin D synergistically reduce HG-enhanced expression of TRAIL in CTLs and coherently protect 1.4E7 cells from TRAIL-mediated apoptosis. Notably, in patients with diabetes, correlation between Vitamin D concentrations in plasma and glucose levels is linked to HG-enhanced TRAIL expression on CTLs. Microarray data reveal that OXCT2, an important enzyme in ketone body catabolism, is a promising target in response to vitamin D. Our work not only reveals a novel mechanism of CTL involvement in progression of diabetes, but also establishes CTLs as a target for combined metformin and vitamin D therapy to protect pancreatic beta cells of diabetic patients.

immunology↗

bioRxiv2021

Weight loss response following lifestyle intervention associated with baseline gut metagenomic signature in humans

We report a weight-loss response analysis on a small cohort of individuals (N=25) selected from a larger population (N[~]5,000) enrolled in a commercial scientific wellness program, which included healthy lifestyle coaching. Each individual had baseline data on blood metabolomics, blood proteomics, clinical labs, lifestyle questionnaires, and stool metagenomes. A subset of these participants (N=15) lost at least 10% of their body weight within a 6-12 month period and saw significant improvement in metabolic health markers ( weight loss group), while another subset of individuals (N=10) undergoing the same lifestyle intervention showed no change in BMI over the same timeframe ( no weight loss group). Only a single baseline blood analyte, a metabolite linked to fried food consumption, was (negatively) associated with weight loss, but a large number of baseline stool metagenomic features, including complex polysaccharide and protein degradation genes, stress-response genes, respiration-related genes, cell wall synthesis genes, and gut bacterial replication rates, were significantly associated with weight loss after explicitly controlling for baseline BMI. Together, these results provide a set of baseline gut microbiome functional features that are associated with weight loss outcomes.

microbiology↗

bioRxiv2020

Gut Microbiome Pattern Reflects Healthy Aging and Predicts Extended Survival in Humans

The gut microbiome has important effects on human health, yet its importance in human aging remains unclear. Using two independent cohorts comprising 4582 individuals across the adult lifespan we demonstrate that, starting in mid-to-late adulthood, gut microbiomes become increasingly unique with age. This uniqueness pattern is strongly associated with gut microbial amino acid derivatives circulating within the bloodstream, many of which have been previously identified as longevity biomarkers. At the latest stages of human life, two distinct patterns emerge wherein individuals in good health show continued microbial drift toward a unique compositional state, while the same drift is absent in individuals who perform worse on a number of validated health measures. The identified healthy aging pattern is characterized by an overall depletion of core genera found across most humans - primarily a depletion in the nearly ubiquitous genus Bacteroides. Consistently, retaining a high Bacteroides dominance into extreme age, or, equivalently, having a low gut microbiome uniqueness score, predicts decreased survival in a four-year follow-up. Our comprehensive analysis identifies the gut microbiome as a novel component of healthy aging, with important implications for the worlds growing older population.

systems biology↗

bioRxiv2019

Negative plant-microbiome feedback limits productivity in aquaponics

The demand for food will outpace productivity of conventional agriculture due to projected growth of the human population, concomitant with shrinkage of arable land, increasing scarcity of freshwater, and a rapidly changing climate. Efforts to increase conventional agricultural output come with significant environmental impacts stemming from deforestation and excessive use of chemicals, including soil salinization, erosion, and nutrient runoffs. While aquaponics has potential to sustainably supplement food production with minimal environmental impact, there is a need to better characterize the complex interplay between the various components (fish, plant, microbiome) of these systems to optimize scale up and productivity. For instance, much of our knowledge of beneficial and detrimental microbial communities vis-a-vis crop productivity comes from studies on plant-microbiome interactions in soil. Here, we investigated how the practice of continued transfer of microbial communities from pre-existing systems might promote or impede productivity of aquaponics. Specifically, we monitored plant growth phenotypes, water chemistry, and microbiome composition of rhizospheres, biofilters, and fish feces over 61-days of lettuce (Lactuca sativa) growth in aquaponic systems inoculated with bacteria that were either commercially sourced or originating from a pre-existing aquaponic system. Strikingly, L. sativa plant and root growth was significantly reduced across all replicates inoculated with the established microbiome. Further analyses revealed the reduced productivity was potentially a consequence of plant-specific pathogen enrichment, including Pseudomonas, through transfer of microbiomes from pre-existing systems - a phenomenon consistent with negative feedbacks in soil ecology. These findings underscore the need for diagnostic tools to monitor microbiome composition, detect negative feedbacks early, and minimize pathogen accumulation in aquaponic systems.

microbiology↗

bioRxiv2019

Progressive shifts in the gut microbiome reflect prediabetes and diabetes development in a treatment-naive Mexican cohort

Type 2 diabetes (T2D) is a global epidemic that affects more than 8% of the worlds population and is a leading cause of death in Mexico. Diet and lifestyle are known to contribute to the onset of T2D. However, the role of the gut microbiome in T2D progression remains uncertain. Associations between microbiome composition and diabetes are confounded by medication use, diet, and obesity. Here we present data on a treatment-naive cohort of 405 Mexican individuals across varying stages of T2D severity. Associations between gut bacteria and more than 200 clinical variables revealed a defined set of bacterial genera that were consistent biomarkers of T2D prevalence and risk. Specifically, gradual increases in blood glucose levels, beta cell dysfunction, and the accumulation of measured T2D risk factors was correlated with the relative abundances of four bacterial genera. In a cohort of 25 individuals, T2D treatment - predominantly metformin - reliably returned the microbiome to the normoglycemic community state. Deep clinical characterization allowed us to broadly control for confounding variables, indicating that these microbiome patterns were independent of common T2D comorbidities, like obesity or cardiovascular disease. Thus, our work provides the first solid evidence for a direct link between the gut microbiome and T2D in a critically high-risk population. Whether or not these T2D-associated changes in the gut contribute to the etiology of T2D or its comorbidities remains to be seen.

systems biology↗

bioRxiv2019

Non-responder phenotype reveals microbiome-wide antibiotic resistance in the murine gut

Broad spectrum antibiotics can cause both transient and lasting damage to the ecology of the gut microbiome. Loss of gut bacterial diversity has been linked to immune dysregulation and disease susceptibility. Antibiotic-resistant populations of cells are known to arise spontaneously in single-strain systems. Furthermore, prior work on subtherapeutic antibiotic treatment in humans and therapeutic treatments in non-human animals have suggested that entire gut communities may exhibit spontaneous resistance phenotypes. In this study, we validate the existence of these community resistance phenotypes in the murine gut and explore how antibiotic duration or diet influence the frequency of this phenotype. We find that almost a third of mice exhibit whole-community resistance to a therapeutic concentration of the {beta}-lactam antibiotic cefoperazone, independent of antibiotic treatment duration or xenobiotic dietary amendment. These non-responder (i.e. resistant) microbiota were protected from biomass depletion, transient ecological community collapse, and lasting diversity loss seen in the susceptible microbiota. There were no major differences between non-responder microbiota and untreated control microbiota at the community structure level. However, gene expression was vastly different between non-responder microbiota and controls during antibiotic treatment, with non-responder communities showing an upregulation of antimicrobial resistance genes and a down-regulation of central metabolism. Thus, non-responder phenotypes appear to combat antibiotic assault through a combination of efflux transporter upregulation and a reduced growth rate across the entire gut community. Future work should focus on what factors are responsible for tipping entire communities from susceptible to resistant phenotypes so that we might harness this phenomenon to protect our microbiota from exposure to therapeutic antibiotic treatment regimes.

systems biology↗

bioRxiv2018

Micom: metagenome-scale modeling to infer metabolicinteractions in the microbiota.

Compositional changes in the gut microbiota have been associated with a variety of medical conditions such as obesity, Crohns disease and diabetes. However, connecting microbial community composition to ecosystem function remains a challenge. Here, we introduce MICOM - a customizable metabolic model of the human gut microbiome. By using a heuristic optimization approach based on L2 regularization we were able to obtain a unique set of realistic growth rates that corresponded well with observed replication rates. We integrated adjustable dietary and taxon abundance constraints to generate personalized metabolic models for individual metagenomic samples. We applied MICOM to a balanced cohort of metagenomes from 186 people, including a metabolically healthy population and individuals with type 1 and type 2 diabetes. Model results showed that individual bacterial genera maintained conserved niche structures across humans, while the community-level production of short chain fatty acids (SCFAs) was heterogeneous and highly individual-specific. Model output revealed complex cross-feeding interactions that would be difficult to measure in vivo. Metabolic interaction networks differed somewhat consistently between healthy and diabetic subjects. In particular MICOM predicted reduced butyrate and propionate production in a diabetic cohort, with restoration of SCFA production profiles found in healthy subjects following metformin treatment. Overall, we found that changes in diet or taxon abundances have highly personalized effects. We believe MICOM can serve as a useful tool for generating mechanistic hypotheses for how diet and microbiome composition influence community function. All methods are implemented in the open source Python package, which is available at https://github.com/micom-dev/micom.

systems biology↗

bioRxiv2018

Memote: A community-driven effort towards a standardized genome-scale metabolic model test suite

Several studies have shown that neither the formal representation nor the functional requirements of genome-scale metabolic models (GEMs) are precisely defined. Without a consistent standard, comparability, reproducibility, and interoperability of models across groups and software tools cannot be guaranteed.\n\nHere, we present memote (https://github.com/opencobra/memote) an open-source software containing a community-maintained, standardized set of metabolic model tests. The tests cover a range of aspects from annotations to conceptual integrity and can be extended to include experimental datasets for automatic model validation. In addition to testing a model once, memote can be configured to do so automatically, i.e., while building a GEM. A comprehensive report displays the models performance parameters, which supports informed model development and facilitates error detection.\n\nMemote provides a measure for model quality that is consistent across reconstruction platforms and analysis software and simplifies collaboration within the community by establishing workflows for publicly hosted and version controlled models.

systems biology↗

bioRxiv2018

Distinct Microbes, Metabolites, and Ecologies Define the Microbiome in Deficient and Proficient Mismatch Repair Colorectal Cancers

Background\n\nThe link between colorectal cancer (CRC) and the gut microbiome has been established, but the specific microbial species and their role in carcinogenesis remain controversial. Our understanding would be enhanced by better accounting for tumor subtype, microbial community interactions, metabolism, and ecology.\n\nMethods\n\nWe collected paired colon tumor and normal-adjacent tissue and mucosa samples from 83 individuals who underwent partial or total colectomies for CRC. Mismatch repair (MMR) status was determined in each tumor sample and classified as either deficient MMR (dMMR) or proficient MMR (pMMR) tumor subtypes. Samples underwent 16S rRNA gene sequencing and a subset of samples from 50 individuals were submitted for targeted metabolomic analysis to quantify amino acids and short-chain fatty acids. A PERMANOVA was used to identify the biological variables that explained variance within the microbial communities. dMMR and pMMR microbial communities were then analyzed separately using a generalized linear mixed effects model that accounted for MMR status, sample location, intra-subject sample correlation, and read depth. Genome-scale metabolic models were then used to generate microbial interaction networks for dMMR and pMMR microbial communities. We assessed global network properties as well as the metabolic influence of each microbe within the dMMR and pMMR networks.\n\nResults\n\nWe demonstrate distinct roles for microbes in dMMR and pMMR CRC. Sulfidogenic Fusobacterium nucleatum and hydrogen sulfide production were significantly enriched in dMMR CRC, but not pMMR CRC. We also surveyed the butyrate-producing microbial species, but did not find a significant difference in predicted or actual butyrate production between dMMR and pMMR microbial communities. Finally, we observed that dMMR microbial communities were predicted to be less stable than pMMR microbial communities. Community stability may play an important role in CRC development, progression, or immune activation within the respective MMR subtypes.\n\nConclusions\n\nIntegrating tumor biology and microbial ecology highlighted distinct microbial, metabolic, and ecological properties unique to dMMR and pMMR CRC. This approach could critically improve our ability to define, predict, prevent, and treat colorectal cancers.

cancer biology↗