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Malta, T.

Publications and source records attributed to Malta, T..

2 recordsLinked to original sources

Cold acclimation reprograms hepatic lipid composition toward n-3 HUFAs to uncouple adipose-derived lipid flux from steatosis

While cold exposure drives lipid flux from adipose tissue to the liver, this enhanced inter-organ crosstalk does not result in sustained hepatic steatosis during prolonged acclimation, indicating that factors beyond lipid flux shape metabolic outcome. To interrogate this adaptation, we performed integrated lipidomic and metabolic profiling across tissues and circulating lipoproteins over the course of cold acclimation. We showed that cold acclimation induces systemic reprogramming of lipid quality in mice, characterized by enrichment of n-3 highly unsaturated fatty acids (HUFAs) as a consequence of upregulation of fatty acid desaturases (FADS1 and 2) in the liver and white adipose tissue, thus increasing hepatic n-3/n-6 ratio. Cold-induced increase in n-3 HUFAs cause the suppression of SCD1-mediated desaturation, thus yielding a depletion of monounsaturated fatty acids (MUFAs) in the liver, along with the suppression of lipogenic markers. Notably, this high-HUFA/low-MUFA lipid signature is present in both hepatic free fatty acid and triglyceride pools, indicating that lipid remodeling occurs upstream of triglyceride synthesis. Lipidomic analysis revealed that the remodeled triglycerides are incorporated into very-low-density and intermediate density lipoproteins (VLDL and IDL), thereby propagating hepatic lipid reprogramming to the circulation. Thus, by selectively increasing endogenous n-3 HUFA availability, cold adaptation suppresses hepatic DNL and MUFA-driven triglyceride assembly, buffering lipid accumulation despite sustained fatty acid influx and reshaping systemic lipid distribution with potential cardiometabolic impact.

biochemistry↗

A Machine Learning One-Class Logistic Regression Model to Predict Stemness in Single Cell Transcriptomics and Spatial Omics Datasets

Cell annotation is a crucial methodological component to interpreting single cell and spatial omics data. These approaches are often biased and manually curated. Here we harness an existing stemness model for assessing oncogenic states to transform its application to single cell and spatial omic datasets. This one-class logistic regression machine learning algorithm is used to extract transcriptomic or proteomic features from non-transformed stem cells to identify dedifferentiated cell states. We found this method identifies single cell states in metastatic tumor cell populations without the requirement of cell annotation. Finally these stemness indices are applicable across a variety of spatial transcriptomic and proteomic technologies for the identification of oncogenic cell types in the tumor microenvironment. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=162 SRC="FIGDIR/small/539461v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@f2973forg.highwire.dtl.DTLVardef@a7aa9corg.highwire.dtl.DTLVardef@1b1ea27org.highwire.dtl.DTLVardef@183b4a5_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗