Biology subjects

Lopez, M. P.

Publications and source records attributed to Lopez, M. P..

3 recordsLinked to original sources

bioRxiv2026

Non-enzymatic ABHD6 interacts with Akt-FoxO1 axis to regulate selective hepatic insulin resistance

The enzymatic function of ABHD6 on insulin secretion and insulin resistance is well documented. However, its non-enzymatic function, especially its effects on selective hepatic insulin resistance and metabolic dysfunction-associated steatotic liver disease (MASLD) is completely unexplored. ABHD6 is elevated under conditions of diet-induced obesity and aging. To define the role of ABHD6 in liver physiology, we generated liver-specific ABHD6 knockout mice, as well as liver specific overexpression of native and enzymatic inactive mutant ABHD6 mouse models. We demonstrated that ABHD6 is an unidentified regulator of selective hepatic insulin resistance and contributes to MASLD and liver fibrosis. Furthermore, we found that non-enzymatic ABHD6, rather than its enzymatic form, contributes to this regulation. Mechanistically, we found that ABHD6 translocated into the nucleus and interacted with Akt/FoxO1 axis to regulate its function. In addition, knockdown of FoxO1 in primary hepatocytes or overexpression of constitutively active mutant FoxO1 by AAV approach could completely abolish the effects of ABHD6 on glucose tolerance and gluconeogenesis. Our study reveals an entirely different mechanism underlying selective hepatic insulin resistance that involves a previously unknown non-enzymatic function of ABHD6. This study opens an avenue for the development of a novel class of ABHD6 inhibitors to treat MASLD and liver fibrosis. HighlightsO_LIABHD6 expression in the liver is increased with obesity and aging. C_LIO_LIABHD6 manipulation affects selective hepatic insulin resistance, MASLD and liver fibrosis. C_LIO_LINon-enzymatic ABHD6 interacts with Akt/FoxO1 axis to regulate FoxO1 transcriptional activity. C_LIO_LIThe effects of ABHD6 on glucose tolerance and hepatic gluconeogenesis are completely dependent on FoxO1 activity. C_LI

physiology↗

bioRxiv2024

ChatNT: A Multimodal Conversational Agent for DNA, RNA and Protein Tasks

Language models are thriving, powering conversational agents that assist and empower humans to solve a number of tasks. Recently, these models were extended to support additional modalities including vision, audio and video, demonstrating impressive capabilities across multiple domains including healthcare. Still, conversational agents remain limited in biology as they cannot yet fully comprehend biological sequences. On the other hand, high-performance foundation models for biological sequences have been built through self-supervision over sequencing data, but these need to be fine-tuned for each specific application, preventing transfer and generalization between tasks. In addition, these models are not conversational which limits their utility to users with coding capabilities. In this paper, we propose to bridge the gap between biology foundation models and conversational agents by introducing ChatNT, the first multimodal conversational agent with an advanced understanding of biological sequences. ChatNT achieves new state-of-the-art results on the Nucleotide Transformer benchmark while being able to solve all tasks at once, in English, and to generalize to unseen questions. In addition, we have curated a new set of more biologically relevant instructions tasks from DNA, RNA and proteins, spanning multiple species, tissues and biological processes. ChatNT reaches performance on par with state-of-the-art specialized methods on those tasks. We also present a novel perplexity-based technique to help calibrate the confidence of our model predictions. Our framework for genomics instruction-tuning can be easily extended to more tasks and biological data modalities (e.g. structure, imaging), making it a widely applicable tool for biology. ChatNT is the first model of its kind and constitutes an initial step towards building generally capable agents that understand biology from first principles while being accessible to users with no coding background.

genomics↗

bioRxiv2022

Impairment of lipid homoeostasis causes accumulation of protein aggregates in the lysosome

Protein aggregation increases during aging and is a pathological hallmark of many age-related diseases. Protein homeostasis (proteostasis) depends on a core network of factors directly influencing protein production, folding, trafficking, and degradation. Cellular proteostasis also depends on the overall composition of the proteome and numerous environmental variables. Modulating this cellular proteostasis state can influence the stability of multiple endogenous proteins, yet the factors contributing to this state remain incompletely characterized. Here, we performed genome-wide CRISPRi screens to elucidate the modulators of proteostasis state in mammalian cells, using a fluorescent dye to monitor endogenous protein aggregation. These screens identified known components of the proteostasis network and uncovered a novel link between protein and lipid homeostasis. Increasing lipid uptake and/or disrupting lipid metabolism promotes the accumulation of sphingomyelins and cholesterol esters and drives the formation of detergent-insoluble protein aggregates at the lysosome. Proteome profiling of lysosomes revealed ESCRT accumulation, suggesting disruption of ESCRT disassembly, lysosomal membrane repair, and microautophagy. Lipid dysregulation leads to lysosomal membrane permeabilization but does not otherwise impact fundamental aspects of lysosomal and proteasomal functions. Together, these results demonstrate that lipid dysregulation disrupts ESCRT function and impairs proteostasis.

cell biology↗