My publications summarized. What did I do and why.

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

2021-01-08 2m read

Type 2 diabetes (T2D) is a global epidemic that affects more than 8% of the world’s 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.

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

2021-01-05 2m read

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).

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

2020-02-26 1m read

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.

MICOM: metagenome-scale modeling to infer metabolic interactions in the microbiota

2020-01-21 2m read

Compositional changes in the gut microbiota have been associated with a variety of medical conditions such as obesity, Crohn’s 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.

Negative plant-microbiome feedback limits productivity in aquaponics

2019-07-27 2m read

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.

Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

2019-07-24 1m read

We present QIIME 2, an open-source microbiome data science platform accessible to users spanning the microbiome research ecosystem, from scientists and engineers to clinicians and policy makers. QIIME 2 provides new features that will drive the next generation of microbiome research. These include interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.

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

2019-04-06 2m read

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.

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

2019-04-06 1m read

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. Here, we present 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.

Distinct microbes, metabolites, and ecologies define the microbiome in deficient and proficient mismatch repair colorectal cancers

2019-04-06 2m read

Background Links between colorectal cancer (CRC) and the gut microbiome have been established, but the specific microbial species and their role in carcinogenesis remain an active area of inquiry. Our understanding would be enhanced by better accounting for tumor subtype, microbial community interactions, metabolism, and ecology. Methods We 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.

Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer

2019-04-06 1m read

Multi-omic data and genome-scale microbial metabolic models have allowed us to examine microbial communities, community function, and interactions in ways that were not available to us historically. Now, one of our biggest challenges is determining how to integrate data and maximize data potential. Our study demonstrates one way in which to test a hypothesis by combining multi-omic data and community metabolic models. Specifically, we assess hydrogen sulfide production in colorectal cancer based on stool, mucosa, and tissue samples collected on and off the tumor site within the same individuals.

Personalized Prediction of Proliferation Rates

2017-01-19 2m read

Cancer is a complex disease and manifests in many different forms. In fact, when speaking about cancer we are probably speaking about thousand different diseases and not one. The high level of heterogeneity between and across different cancer subtypes requires large amounts of data to study them. Luckily, we do have large data sets today. However, the kind of knowledge we can extract from those data sets depends a lot on where we got the data from.

Going from metabolites to affected enzymes in cancer

2016-06-21 2m read

When studying cancer or any disease one of the things we are interested in are the alterations that cause the disease. By now, we have quite some arsenal to study genomic aberrations, however assigning those to a specific phenotype is not trivial. This is particularly true for changes affecting metabolism, since there is a myriad of regulation events that take place after gene expression and which drive metabolism. The image above shows just an example of events that can happen between translation of an enzyme gene until it will finally catalyze a reaction.

Building multifunctional peptides by compatible function

2016-04-21 1m read

Artificially designed small peptides are currently quite interesting for medical research since they provide a way to target specific cellular activities. In many cases one wants to combine several functions into a single peptide that is as small as possible. This is problematic as the activity of the peptide is often lost when combining several functions. In our article we propose a design strategy based on “compatible function”, meaning functions that require similar physio-chemical properties of the peptide.

How yeast creates their own signaling landscape

2015-08-28 1m read

Signaling in yeast is often used as a blueprint for human signaling pathways since its general way of function is close to what we can observe in human. This is not only true for signaling pathways within a single yeast cell but also for the ways yeast cells communicate with each other. During the mating of yeast, individual cells communicate their position using distinct pheromones. However, at the same time they also secrete a protein that destroys those very pheromones and thos paradoxically counteracts this signaling.

CPPs and CAPs: Two sides of the same coin

2014-03-25 1m read

Cell penetrating peptides (CPP) and cationic antibacterial peptides (CAP) have similar physicochemical properties and yet it is not understood how such similar peptides display different activities. To address this question, we used Iztli peptide 1 (IP-1) because it has both CPP and CAP activities. Combining experimental and computational modeling of the internalization of IP-1, we show it is not internalized by receptor-mediated endocytosis, yet it permeates into many different cell types, including fungi and human cells.

Immunogenic variety and the Golden Agers

2012-11-30 2m read

The immune system protects us from foreign substances or pathogens by generating specific antibodies. The variety of immunoglobulin (Ig) paratopes for antigen recognition is a result of the V(D)J rearrangement mechanism, while a fast and efficient immune response is mediated by specific immunoglobulin isotypes obtained through class switch recombination (CSR). To get a better understanding on how antibody-based immune protection works and how it changes with age, the interdependency between these two parameters need to be addressed.

Ensuring low noise in the yeast cell cycle

2011-10-07 2m read

The budding yeast genome comprises roughly 6000 genes generating a number of about 10 000 mRNA copies, which gives a general estimation of 1-2 mRNA copies generated per gene. What does this observation implicate for cellular processes and their regulation? Whether the number of mRNA molecules produced is important for setting the amount of proteins implicated in a particular function is at present unknown. In this context, we studied cell cycle control as one of the highly fine tuned processes that guarantee the precise timing of events essential for cell growth.

What influences DNA replication rate in budding yeast?

2010-04-27 2m read

DNA replication begins at specific locations called replication origins, where helicase and polymerase act in concert to unwind and process the single DNA filaments. The sites of active DNA synthesis are called replication forks. The density of initiation events is low when replication forks travel fast, and is high when forks travel slowly. Despite the potential involvement of epigenetic factors, transcriptional regulation and nucleotide availability, the causes of differences in replication times during DNA synthesis have not been established satisfactorily, yet.