Lisa Lamberti

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Lamberti

First Name

Lisa

ORCID

0000-0002-6381-1316

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Publications 1 - 10 of 10

A geometric interpretation of the triangulated structure of m-cluster categories
Lamberti, Lisa (2014)
Communications in Algebra
Cluster tilting modules for mesh algebras
Erdmann, Karin; Gratz, Sira; Lamberti, Lisa (2021)
Linear Algebra and its Applications
We study cluster tilting modules in mesh algebras of Dynkin type as defined in [12], providing a new proof for their existence. Except for type G2, we show that these are precisely the maximal rigid modules, and that they are equivariant for a certain automorphism. We further study their mutation, providing an example of mutation in an abelian category which is not stably 2-Calabi-Yau, and explicitly describe the combinatorics.
Master regulators of biological systems in higher dimensions
Eble, Holger; Joswig, Michael; Lamberti, Lisa; et al. (2023)
Proceedings of the National Academy of Sciences of the United States of America
A longstanding goal of biology is to identify the key genes and species that critically impact evolution, ecology, and health. Network analysis has revealed keystone species that regulate ecosystems and master regulators that regulate cellular genetic networks. Yet these studies have focused on pairwise biological interactions, which can be affected by the context of genetic background and other species present, generating higher-order interactions. The important regulators of higher-order interactions are unstudied. To address this, we applied a high-dimensional geometry approach that quantifies epistasis in a fitness landscape to ask how individual genes and species influence the interactions in the rest of the biological network. We then generated and also reanalyzed 5-dimensional datasets (two genetic, two microbiome). We identified key genes (e.g., the rbs locus and pykF) and species (e.g., Lactobacilli) that control the interactions of many other genes and species. These higher-order master regulators can induce or suppress evolutionary and ecological diversification by controlling the topography of the fitness landscape. Thus, we provide a method and mathematical justification for exploration of biological networks in higher dimensions.
Novel Insights into the Aortic Mechanical Properties of Mice Modeling Hereditary Aortic Diseases
Dubacher, Nicolo; Sugiyama, Kaori; Smith, Jeffrey D.; et al. (2025)
Thrombosis and Haemostasis
Objective: Hereditary aortic diseases (hADs) increase the risk of aortic dissections and ruptures. Recently, we have established an objective approach to measure the rupture force of the murine aorta, thereby explaining the outcomes of clinical studies and assessing the added value of approved drugs in vascular Ehlers–Danlos syndrome (vEDS). Here, we applied our approach to six additional mouse hAD models. Material and Methods: We used two mouse models (Fbn1^C1041G and Fbn1^mgR ) of Marfan syndrome (MFS) as well as one smooth-muscle-cell-specific knockout (SMKO) of Efemp2 and three CRISPR/Cas9-engineered knock-in models (Ltbp1, Mfap4, and Timp1). One of the two MFS models was subjected to 4-week-long losartan treatment. Per mouse, three rings of the thoracic aorta were prepared, mounted on a tissue puller, and uniaxially stretched until rupture. Results: The aortic rupture force of the SMKO and both MFS models was significantly lower compared with wild-type mice but in both MFS models higher than in mice modeling vEDS. In contrast, the Ltbp1, Mfap4, and Timp1 knock-in models presented no impaired aortic integrity. As expected, losartan treatment reduced aneurysm formation but surprisingly had no impact on the aortic rupture force of our MFS mice. Conclusion: Our read-out system can characterize the aortic biomechanical integrity of mice modeling not only vEDS but also related hADs, allowing the aortic-rupture-force-focused comparison of mouse models. Furthermore, aneurysm progression alone may not be a sufficient read-out for aortic rupture, as antihypertensive drugs reducing aortic dilatation might not strengthen the weakened aortic wall. Our results may enable identification of improved medical therapies of hADs.
Microbiome interactions shape host fitness
Gould, Alison L.; Zhang, Vivian; Lamberti, Lisa; et al. (2018)
Proceedings of the National Academy of Sciences of the United States of America
Gut bacteria can affect key aspects of host fitness, such as development, fecundity, and lifespan, while the host, in turn, shapes the gut microbiome. However, it is unclear to what extent individual species versus community interactions within the microbiome are linked to host fitness. Here, we combinatorially dissect the natural microbiome of Drosophila melanogaster and reveal that interactions between bacteria shape host fitness through life history tradeoffs. Empirically, we made germ-free flies colonized with each possible combination of the five core species of fly gut bacteria. We measured the resulting bacterial community abundances and fly fitness traits, including development, reproduction, and lifespan. The fly gut promoted bacterial diversity, which, in turn, accelerated development, reproduction, and aging: Flies that reproduced more died sooner. From these measurements, we calculated the impact of bacterial interactions on fly fitness by adapting the mathematics of genetic epistasis to the microbiome. Development and fecundity converged with higher diversity, suggesting minimal dependence on interactions. However, host lifespan and microbiome abundances were highly dependent on interactions between bacterial species. Higher-order interactions (involving three, four, and five species) occurred in 13–44% of possible cases depending on the trait, with the same interactions affecting multiple traits, a reflection of the life history tradeoff. Overall, we found these interactions were frequently context-dependent and often had the same magnitude as individual species themselves, indicating that the interactions can be as important as the individual species in gut microbiomes.
Within-patient genetic diversity of SARS-CoV-2
Kuipers, Jack; Batavia, Aashil A.; Jablonski, Kim Philipp; et al. (2020)
bioRxiv
SARS-CoV-2, the virus responsible for the current COVID-19 pandemic, is evolving into different genetic variants by accumulating mutations as it spreads globally. In addition to this diversity of consensus genomes across patients, RNA viruses can also display genetic diversity within individual hosts, and co-existing viral variants may affect disease progression and the success of medical interventions. To systematically examine the intra-patient genetic diversity of SARS-CoV-2, we processed a large cohort of 3939 publicly-available deeply sequenced genomes with specialised bioinformatics software, along with 749 recently sequenced samples from Switzerland. We found that the distribution of diversity across patients and across genomic loci is very unbalanced with a minority of hosts and positions accounting for much of the diversity. For example, the D614G variant in the Spike gene, which is present in the consensus sequences of 67.4% of patients, is also highly diverse within hosts, with 29.7% of the public cohort being affected by this coexistence and exhibiting different variants. We also investigated the impact of several technical and epidemiological parameters on genetic heterogeneity and found that age, which is known to be correlated with poor disease outcomes, is a significant predictor of viral genetic diversity.
Repetitive higher cluster categories of type An
Lamberti, Lisa (2014)
Journal of Algebra and Its Applications
We show that the repetitive higher cluster category of type An, deﬁned as the orbit category Db(mod kAn)/(τ−1[m])p, is equivalent to a category deﬁned on a subset of diagonals in a regular polygon. This generalizes the construction of Caldero–Chapoton–Schiﬄer [Quivers with relations arising from clusters (Ancase), Trans. Amer. Math.Soc. 358(3) (2006) 1347–1364], which we recover when p = m =1,and the work of Baur–Marsh, [A geometric description of the m-cluster categories, Trans. Amer. Math.Soc. 360(11) (2008) 5789–5803], treating the case p =1,m>1. Our approach also leads to a geometric model of the bounded derived category in type A.
The geometry of partial fitness orders and an efficient method for detecting genetic interactions
Lienkaemper, Caitlin; Lamberti, Lisa; Drain, James; et al. (2018)
Journal of Mathematical Biology
We present an efficient computational approach for detecting genetic interactions from fitness comparison data together with a geometric interpretation using polyhedral cones associated to partial orderings. Genetic interactions are defined by linear forms with integer coefficients in the fitness variables assigned to genotypes. These forms generalize several popular approaches to study interactions, including Fourier–Walsh coefficients, interaction coordinates, and circuits. We assume that fitness measurements come with high uncertainty or are even unavailable, as is the case for many empirical studies, and derive interactions only from comparisons of genotypes with respect to their fitness, i.e. from partial fitness orders. We present a characterization of the class of partial fitness orders that imply interactions, using a graph-theoretic approach. Our characterization then yields an efficient algorithm for testing the condition when certain genetic interactions, such as sign epistasis, are implied. This provides an exponential improvement of the best previously known method. We also present a geometric interpretation of our characterization, which provides the basis for statistical analysis of partial fitness orders and genetic interactions.
On cluster categories and related topics
Lamberti, Lisa (2013)
Cluster partitions and fitness landscapes of the Drosophila fly microbiome
Eble, Holger; Joswig, Michael; Lamberti, Lisa; et al. (2019)
Journal of Mathematical Biology
The concept of genetic epistasis defines an interaction between two genetic loci as the degree of non-additivity in their phenotypes. A fitness landscape describes the phenotypes over many genetic loci, and the shape of this landscape can be used to predict evolutionary trajectories. Epistasis in a fitness landscape makes prediction of evolutionary trajectories more complex because the interactions between loci can produce local fitness peaks or troughs, which changes the likelihood of different paths. While various mathematical frameworks have been proposed to investigate properties of fitness landscapes, Beerenwinkel et al. (Stat Sin 17(4):1317–1342, 2007a) suggested studying regular subdivisions of convex polytopes. In this sense, each locus provides one dimension, so that the genotypes form a cube with the number of dimensions equal to the number of genetic loci considered. The fitness landscape is a height function on the coordinates of the cube. Here, we propose cluster partitions and cluster filtrations of fitness landscapes as a new mathematical tool, which provides a concise combinatorial way of processing metric information from epistatic interactions. Furthermore, we extend the calculation of genetic interactions to consider interactions between microbial taxa in the gut microbiome of Drosophila fruit flies. We demonstrate similarities with and differences to the previous approach. As one outcome we locate interesting epistatic information on the fitness landscape where the previous approach is less conclusive.

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Lisa Lamberti

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