Biology subjects

Gruber, K.

Publications and source records attributed to Gruber, K..

5 recordsLinked to original sources

bioRxiv2026

Triacylglycerol fatty acid composition modulates ATGL regioselectivity and diacylglycerol output

Adipose triglyceride lipase (ATGL) catalyses the first and rate-limiting step of intracellular triacylglycerol (TAG) hydrolysis, yielding diacylglycerol (DAG) and a free fatty acid (FA). The site of TAG cleavage determines the regioisomeric identity of the DAG product, with distinct consequences for downstream signalling and lipid metabolism. Here, we show that ATGL prefers specific fatty acyl chain lengths and that this preference contributes to its regioselectivity on heterogeneous TAG substrates. Structural modelling identified a substrate-binding pocket comprising a hydrophilic anchor and a hydrophobic tunnel whose dimensions favour long-chain FAs. Mass spectrometry-based analysis of DAG products generated by ATGL from defined TAG substrates revealed an intrinsic preference for terminal (sn-1/sn-3) ester bond cleavage. Acyl-chain compatibility modulated this preference, with poorly accommodated terminal chains increasing the relative contribution of sn-2 cleavage. Consequently, ATGL generated predominantly sn-2-retaining DAG species from heterogeneous TAG substrates containing long-chain fatty acyl chains, including substantial amounts of the sn-1,2-DAG regioisomer, which has not previously been associated with ATGL activity. These findings link the fatty acyl chain composition of TAGs to the DAG regioisomer profile generated by ATGL-mediated lipolysis and provide substrate-dependent parameters for more species-resolved models of lipid metabolism.

biochemistry↗

bioRxiv2026

CLN6 is a glycerophosphoglycerol epimerase in bis(monoacylglycero)phosphate biosynthesis

Lysosomal catabolism depends on bis(monoacylglycero)phosphate (BMP), a phospholipid with atypical S,S stereochemistry of unknown biosynthetic origin. CLN8 and CLN5 catalyze sequential acylation steps in BMP synthesis, and their loss of function causes Batten disease, a group of inherited neurodegenerative lysosomal storage disorders. Here we identify CLN6, a biochemically uncharacterized endoplasmic reticulum protein whose loss also causes Batten disease, as the calcium-dependent glycerophosphoglycerol epimerase that establishes BMP stereochemistry upstream of CLN8. CLN6 deficiency depletes BMP in cells, mice, and patient lysosomes, and reduces lysosomal hydrolase levels. BMP-precursor supplementation bypasses CLN6, restores BMP and increases hydrolase abundance in CLN6-deficient cells and mice. These findings place three Batten disease proteins in a single biosynthetic pathway and reveal BMP deficiency as a reversible cause of disrupted lysosomal hydrolase homeostasis.

biochemistry↗

bioRxiv2026

Acid phospholipase A1 promotes lysosomal membrane catabolism

The molecular mechanisms of lysosomal glycerophospholipid (GPL) catabolism are incompletely understood. Here, we report that acid phospholipase A1 (APLA1), formerly known as palmitoyl-protein thioesterase 2 (PPT2), is required for efficient GPL degradation. Deletion of APLA1 in human cells results in excess accumulation of phospholipids within lysosomes # a pathological condition termed phospholipidosis. APLA1 activity depends on interactions with negatively charged GPLs and is inhibited by phospholipidosis-inducing cationic amphiphilic drugs. Hydrolysis of zwitterionic, but not anionic, GPLs requires co-activation of APLA1 by the lysosome-specific lipid bis(monoacylglycero)phosphate. Upon pharmacological mTORC inhibition, which increases lysosomal GPL turnover, APLA1-deficient cells exhibit massive accumulation of multilamellar membranes in lysosomes and reduced cytosolic triacylglycerol stores. APLA1 acts in concert with lysosomal phospholipase A2 (PLA2G15). Combined APLA1/PLA2G15-deficiency leads to a severe reduction in acid phospholipase A1/A2 activity, thereby exacerbating phospholipidosis. Our observations provide detailed mechanistic insights into lysosomal GPL catabolism, a crucial pathway for maintaining lipid homeostasis.

biochemistry↗

bioRxiv2023

Flattening the curve - How to get better results with small deep-mutational-scanning datasets

Proteins are utilized in various biotechnological applications, often requiring the optimization of protein properties by introducing specific amino acid exchanges. Deep mutational scanning (DMS) is an effective high-throughput method for evaluating the effects of these exchanges on protein function. DMS data can then inform the training of a neural network to predict the impact of mutations. Most approaches employ some representation of the protein sequence for training and prediction. As proteins are characterized by complex structures and intricate residue interaction networks, directly providing structural information as input reduces the need to learn these features from the data. We introduce a method for encoding protein structures as stacked 2D contact maps, which capture residue interactions, their evolutionary conservation, and mutation-induced interaction changes. Furthermore, we explored techniques to augment neural network training performance on smaller DMS datasets. To validate our approach, we trained three neural network architectures originally used for image analysis on three DMS datasets, and we compared their performances with networks trained solely on protein sequences. The results confirm the effectiveness of the protein structure encoding in machine learning efforts on DMS data. Using structural representations as direct input to the networks, along with data augmentation and pre-training, significantly reduced demands on training data size and improved prediction performance, especially on smaller datasets, while performance on large datasets was on par with state-of-the-art sequence convolutional neural networks. The methods presented here have the potential to provide the same workflow as DMS without the experimental and financial burden of testing thousands of mutants. Additionally, we present an open-source, user-friendly software tool to make these data analysis techniques accessible, particularly to biotechnology and protein engineering researchers who wish to apply them to their mutagenesis data.

bioinformatics↗

bioRxiv2021

SARS-CoV-2 Entry Protein TMPRSS2 and Its Homologue, TMPRSS4 Adopts Structural Fold Similar to Blood Coagulation and Complement Pathway Related Proteins

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) utilizes TMPRSS2 receptor to enter target human cells and subsequently causes coronavirus disease 19 (COVID-19). TMPRSS2 belongs to the type II serine proteases of subfamily TMPRSS, which is characterized by the presence of the serine-protease domain. TMPRSS4 is another TMPRSS member, which has a domain architecture similar to TMPRSS2. TMPRSS2 and TMPRSS4 have been shown to be involved in SARS-CoV-2 infection. However, their normal physiological roles have not been explored in detail. In this study, we analyzed the amino acid sequences and predicted 3D structures of TMPRSS2 and TMPRSS4 to understand their functional aspects at the protein domain level. Our results suggest that these proteins are likely to have common functions based on their conserved domain organization. Furthermore, we show that the predicted 3D structure of their serine protease domain has significant similarity to that of plasminogen which dissolves blood clot, and of other blood coagulation related proteins. Additionally, molecular docking analyses of inhibitors of four blood coagulation and anticoagulation factors show the same high specificity to TMPRSS2 and TMPRSS4 3D structures. Hence, our observations are consistent with the blood coagulopathy observed in COVID-19 patients and their predicted functions based on the sequence and structural analyses offer avenues to understand better and explore therapeutic approaches for this disease.

bioinformatics↗