Biology subjects

Lamberti, L.

Publications and source records attributed to Lamberti, L..

5 recordsLinked to original sources

bioRxiv2023

Novel Insights into the Aortic Mechanical Properties of Mice Modeling Hereditary Aortic Diseases

OBJECTIVEHereditary 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. APPROACH AND RESULTWe used two mouse models 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. 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. CONCLUSIONSOur 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. HighlightsO_LIAssay to measure the aortic rupture force as read-out for the biomechanical integrity identified weakened murine thoracic aorta prior to micro- and macroscopic changes in Fbn1+/C1041G mice. C_LIO_LIDespite reducing aneurysm growth, losartan treatment did not have any impact on the aortic rupture force of Fbn1mgR/mgR mice modeling MFS. C_LIO_LISmooth-muscle-cell-knock-out of Efemp2 significantly impaired the rupture force of the murine ascending aorta. C_LIO_LIAortic rupture force measurements could clarify the causality of novel candidate gene(s)/variant(s) in mouse models of aortic diseases. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/553452v2_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@a29d79org.highwire.dtl.DTLVardef@10a272org.highwire.dtl.DTLVardef@19071forg.highwire.dtl.DTLVardef@122ab40_HPS_FORMAT_FIGEXP M_FIG C_FIG

pathology↗

bioRxiv2021

High dimensional geometry of fitness landscapes identifies master regulators of evolution and the microbiome

A longstanding goal of biology is to identify the key genes and species that critically impact evolution, ecology, and health. Yet biological interactions between genes (1, 2), species (3-6), and different environmental contexts (7-9) change the individual effects due to non-additive interactions, known as epistasis. In the fitness landscape concept, each gene/organism/environment is modeled as a separate biological dimension (10), yielding a high dimensional landscape, with epistasis adding local peaks and valleys to the landscape. Massive efforts have defined dense epistasis networks on a genome-wide scale (2), but these have mostly been limited to pairwise, or two-dimensional, interactions (11). Here we develop a new mathematical formalism that allows us to quantify interactions at high dimensionality in genetics and the microbiome. We then generate and also reanalyze combinatorically complete datasets (two genetic, two microbiome). In higher dimensions, we find that key genes (e.g. pykF) and species (e.g. Lactobacillus plantarum) distort the fitness landscape, changing the interactions for many other genes/species. These distortions can fracture a "smooth" landscape with one optimal fitness peak into a landscape with many local optima, regulating evolutionary or ecological diversification (12), which may explain how a probiotic bacterium can stabilize the gut microbiome.

systems biology↗

bioRxiv2020

Within-patient genetic diversity of SARS-CoV-2

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. Author SummarySince it arose in late 2019, the new coronavirus (SARS-CoV-2) behind the COVID-19 pandemic has mutated and evolved during its global spread. Individual patients may host different versions, or variants, of the virus, hallmarked by different mutations. We examine the diversity of genetic variants coexisting within patients across a cohort of 3939 publicly accessible samples and 749 recently sequenced samples from Switzerland. We find that a small number of patients carry most of the diversity, and that patients with more diversity tend to be older. We also find that most of the diversity is concentrated in certain regions and positions of the virus genome. In particular, we find that a variant reported to increase infectivity is among the most diverse positions. Our study provides a large-scale survey of within-patient diversity of the SARS-CoV-2 genome.

genomics↗

bioRxiv2017

High-dimensional microbiome interactions shape host fitness

Gut bacteria can affect key aspects of host fitness, such as development, fecundity, and lifespan, while the host in turn shapes the gut microbiome. Microbiomes co-evolve with their hosts and have been implicated in host speciation. 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. We find that the same microbial interactions that shape host fitness also shape microbiome abundances, suggesting a potential evolutionary mechanism by which microbiome communities (rather than just individual species) may be intertwined in co-selection with their hosts. 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. Host physiology phenotypes were highly dependent on interactions between bacterial species. Higher-order interactions (involving 3, 4, and 5 species) were widely prevalent and impacted both host physiology and the maintenance of gut diversity. The parallel impacts of bacterial interactions on the microbiome and on host fitness suggest that microbiome interactions may be key drivers of evolution.\n\nSignificanceAll animals have associated microbial communities called microbiomes that can influence the physiology and fitness of their host. It is unclear to what extent individual microbial species versus ecology of the microbiome influences fitness of the host. Here we mapped all the possible interactions between individual species of bacteria with each other and with the hosts physiology. Our approach revealed that the same bacterial interactions that shape microbiome abundances also shape host fitness traits. This relationship provides a feedback that may favor the emergence of co-evolving microbiome-host units.

microbiology↗

bioRxiv2017

The geometry of partial fitness orders and an efficient method for detecting genetic interactions

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.

evolutionary biology↗