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Biology subjects

Mathur, L.

Publications and source records attributed to Mathur, L..

2 recordsLinked to original sources

Interrogating Metabolic Interactions Between Skeletal Muscle and Liver Circadian Clocks In Vivo

Expressed throughout the body, the circadian clock system achieves daily metabolic homeostasis at every level of physiology, with clock disruption associated with metabolic disease (1, 2). Molecular clocks present in the brain, liver, adipose, pancreas and skeletal muscle each contribute to glucose homeostasis (3). However, it is unclear; 1) which organ clocks provide the most essential contributions, and 2) if these contributions depend on inter-organ communication. We recently showed that the liver clock alone is insufficient for most aspects of daily liver glucose handling and requires connections with other clocks (4). Considering the pathways that link glucose metabolism between liver and skeletal muscle, we sought to test whether a clock connection along this axis is important. Using our previous published methodology for tissue-specific rescue of Bmal1 in vivo (4, 5), we now show that in the absence of feeding-fasting cycles, liver and muscle clocks are not sufficient for systemic glucose metabolism, nor do they form a functional connection influencing local glucose handling or daily transcriptional rhythms in each tissue. However, the introduction of a daily feeding-fasting rhythm enables a synergistic state between liver and muscle clocks that leads to restoration of systemic glucose tolerance. These findings reveal limited autonomous capabilities of liver and muscle clocks and highlight the need for inter-organ clock communication for glucose homeostasis which involves at least two peripheral metabolic organs.

cell biology↗

Combi-Seq: Multiplexed transcriptome-based profiling of drug combinations using 1deterministic barcoding in single-cell droplets

Anti-cancer therapies often exhibit only short-term effects. Tumors typically develop drug resistance causing relapses that might be tackled with drug combinations. Identification of the right combination is challenging and would benefit from high-content, high-throughput combinatorial screens directly on patient biopsies. However, such screens require a large amount of material, normally not available from patients. To address these challenges, we developed a scalable microfluidic workflow to screen hundreds of drug combinations in picoliter-size droplets using transcriptome changes as a readout for drug effects. We devised a deterministic combinatorial DNA barcoding approach to encode treatment conditions, enabling the gene expression-based readout of drug effects in a highly multiplexed fashion. We applied our method to screen the effect of 420 drug combinations on the transcriptome of K562 cells using only [~]250 single cell droplets per condition, to successfully predict synergistic and antagonistic drug pairs, as well as their pathway activities.

systems biology↗