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Larhlimi, A.

Publications and source records attributed to Larhlimi, A..

4 recordsLinked to original sources

Comparative Transcriptomic Analysis of Obesity and Lipodystrophy Reveals Shared Mechanisms and Novel Targets in Adipose Tissue Dysfunction

The global rise in obesity poses a major public health challenge. While chronic energy surplus is a well-established driver of weight gain and obesity, the mechanisms linking adipose tissue (AT) expansion to cardiometabolic complications remain incompletely understood. In obese individuals, dysfunctional AT loses its capacity to store excess lipids, leading to ectopic fat accumulation and contributing to cardiometabolic complications such as type 2 diabetes. However, the molecular events that drive the transition from healthy to dysfunctional adipocytes are poorly defined. At the opposite end of the adiposity spectrum, lipodystrophies represent a heterogeneous group of disorders characterized by selective loss of AT, often accompanied by severe metabolic disturbances. Despite these contrasting adipose phenotypes, both obesity and lipodystrophy result in similar metabolic complications. In this study, we investigated whether AT in these two contrasting conditions shares a common molecular signature. We performed an unbiased comparative transcriptomic analysis of AT from lipodystrophic BSCL2-deficient and obese mice, identifying a shared signature of 129 genes. Using publicly available datasets, we replicated this signature and refined it to 102 genes whose expression is consistently altered in both obese and lipodystrophic adipose tissue. Correlation network analysis, gene ontology, and literature-based refinement revealed that these genes fall into nine functional categories: lipogenesis, adipocyte differentiation, carbohydrate metabolism, mitochondrial function, amino acid metabolism, reactive oxygen species, metabolic processes, immune response, and a group with no clear functional association. Most of these genes expression levels correlated strongly with insulin sensitivity across lipodystrophic and obese mice, as well as human samples. Finally, 52 genetic loci containing these genes harbor variants associated with type 2 diabetes, including 11 loci where genetic associations directly influence candidate gene expression levels. In conclusion, our findings demonstrate that a shared "energetic collapse" of adipocytes, characterized by profound metabolic inflexibility in pathways spanning glucose utilization, lipogenesis, and amino acid catabolism, represents a common pathogenic mechanism underlying adipose tissue dysfunction in both obesity and lipodystrophy. This convergent molecular signature underscores the critical role of intrinsic adipocyte metabolic health in systemic energy homeostasis and insulin sensitivity.

physiology↗

Planetary-scale marine community modeling predicts metabolic synergy and viral impacts

The oceans buffer against climate change via biogeochemical cycles underpinned by microbial metabolic activities. While planetary-scale surveys provide baseline microbiome data, inferring metabolic and biogeochemical impacts remains challenging. Genome-scale modeling has addressed analogous issues at the cellular level, highlighting key metabolic reactions contingent upon specific environmental conditions. Here we adapt this mechanistic modeling framework towards analyzing global ocean microbial communities to reveal metabolic processes predicted to maintain ecosystem functioning. To achieve this, we developed a genome-scale superorganism metabolic model for each TARA Ocean metagenome or metatranscriptome (i.e., limited to reactions known from heterotrophic prokaryotes and viruses), and evaluated these models to establish a community-wide metabolic phenotype for each sample. To validate, we showed that even with reaction-mappable genes only ([~]1/4 of the total genes), model composition revealed metabolism-inferred ecological zones that matched taxonomy-inferred zones. Model inferred metabolic phenotypes revealed reaction cooperation associated with microbial metabolism and organism diversity. These phenotypes also suggest elevated ecological roles for viruses as model predictions suggest they genomically target community-critical metabolic reactions that underpin metabolic phenotype stability, and also demonstrate that, as metabolites are better understood, immediate estimates could be made for where viruses remineralize versus sink carbon. While this new constraints-based, agile, and mechanistic modeling framework is highly upgradable, it already begins to convert molecular-scale environmental omics data to ecological and even planetary-scale biogeochemical features that will better bring microbes and their viruses into Earth system and climate models.

systems biology↗

Reconciliation and Evolution of Penicillium rubens Genome-Scale Metabolic Networks - What about Specialised Metabolism?

In recent years, genome sequencing of filamentous fungi has revealed a high proportion of specialised metabolites with growing pharmaceutical interest. However, detecting such metabolites through in silico genome analysis does not necessarily guarantee their expression under laboratory conditions. However, one plausible strategy for enabling their production lies in modifying the growth conditions. Devising a comprehensive experimental design testing in different culture environments is time-consuming and expensive. Therefore, using in silico modelling as a preliminary step, such as Genome-Scale Metabolic Network (GSMN), represents a promising approach to predicting and understanding the observed specialised metabolite production in a given organism. To address these questions, we reconstructed a new high-quality GSMN for the Penicillium rubens Wisconsin 54-1255 strain, a commonly used model organism. Our reconstruction, iPrub22, adheres to current convention standards and quality criteria, incorporating updated functional annotations, orthology searches with different GSMN templates, data from previous reconstructions, and manual curation steps targeting primary and specialised metabolites. With a MEMOTE score of 74% and a metabolic coverage of 45%, iPrub22 includes 5,192 unique metabolites interconnected by 5,919 reactions, of which 5,033 are supported by at least one genomic sequence. Of the metabolites present in iPrub22, 13% are categorised as belonging to specialised metabolism. While our high-quality GSMN provides a valuable resource for investigating known phenotypes expressed in P. rubens, our analysis identifies bottlenecks related, in particular, to the definition of what is a specialised metabolite, which requires consensus within the scientific community. It also points out the necessity of accessible, standardised and exhaustive databases of specialised metabolites. These questions must be addressed to fully unlock the potential of natural product production in P. rubens and other filamentous fungi. Our work represents a foundational step towards the objective of rationalising the production of natural products through GSMN modelling.

bioinformatics↗

Contribution of genome scale metabolic modeling to niche theory

Standard niche modeling is based on probabilistic inference from organismal occurrence data but does not benefit yet from genome-scale descriptions of these organisms. This study over-comes this shortcoming by proposing a new conceptual niche that encompasses the whole metabolic capabilities of an organism. The so-called metabolic niche resumes well-known traits such as nutrient needs and their dependencies for survival. Despite the computational challenge, its implementation allows the detection of traits and the formal comparison of niches of different organisms, emphasizing that the presence-absence of functional genes is not enough to approximate the phenotype. Further statistical exploration of an organisms niche sheds light on genes essential for the metabolic niche and their role in understanding various biological experiments, such as transcriptomics, paving the way for incorporating better the genome-scale description in ecological studies.

ecology↗