Jun Lu

Bio: Jun Lu is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Medicine & Materials science. The author has an hindex of 135, co-authored 1526 publications receiving 99767 citations. Previous affiliations of Jun Lu include Drexel University & Argonne National Laboratory.

Characterization of the mammalian miRNA turnover landscape

Abstract: Steady state cellular microRNA (miRNA) levels represent the balance between miRNA biogenesis and turnover. The kinetics and sequence determinants of mammalian miRNA turnover during and after miRNA maturation are not fully understood. Through a large-scale study on mammalian miRNA turnover, we report the co-existence of multiple cellular miRNA pools with distinct turnover kinetics and biogenesis properties and reveal previously unrecognized sequence features for fast turnover miRNAs. We measured miRNA turnover rates in eight mammalian cell types with a combination of expression profiling and deep sequencing. While most miRNAs are stable, a subset of miRNAs, mostly miRNA*s, turnovers quickly, many of which display a two-step turnover kinetics. Moreover, different sequence isoforms of the same miRNA can possess vastly different turnover rates. Fast turnover miRNA isoforms are enriched for 5′ nucleotide bias against Argonaute-(AGO)-loading, but also additional 3′ and central sequence features. Modeling based on two fast turnover miRNA*s miR-222-5p and miR-125b-1-3p, we unexpectedly found that while both miRNA*s are associated with AGO, they strongly differ in HSP90 association and sensitivity to HSP90 inhibition. Our data characterize the landscape of genome-wide miRNA turnover in cultured mammalian cells and reveal differential HSP90 requirements for different miRNA*s. Our findings also implicate rules for designing stable small RNAs, such as siRNAs.

Identification and Characterization of a Sleep-Active Cell Group in the Rostral Medullary Brainstem

Abstract: Early transection and stimulation studies suggested the existence of sleep-promoting circuitry in the medullary brainstem, yet the location and identity of the neurons comprising this putative hypnogenic circuitry remains unresolved. In the present study, we sought to uncover the location and identity of medullary neurons that might contribute to the regulation of sleep. Here we show the following in rats: (1) a delimited node of medullary neurons located lateral and dorsal to the facial nerve—a region we termed the parafacial zone (PZ)—project to the wake-promoting medial parabrachial nucleus; (2) PZ neurons express c-Fos after sleep but not after wakefulness and hence are sleep active; and (3) cell-body-specific lesions of the PZ result in large and sustained increases (50%) in daily wakefulness at the expense of slow-wave sleep (SWS). Using transgenic reporter mice [vesicular GABA/glycine transporter ( Vgat )–GFP], we then show that >50% of PZ sleep-active neurons are inhibitory (GABAergic/glycinergic, VGAT-positive) in nature. Finally, we used a Cre-expressing adeno-associated viral vector and conditional Vgat lox/lox mice to selectively and genetically disrupt GABA/glycinergic neurotransmission from PZ neurons. Disruption of PZ GABAergic/glycinergic neurotransmission resulted in sustained increases (40%) in daily wakefulness at the expense of both SWS and rapid eye movement sleep. These results together reveal the location and neurochemical identity of a delimited node of sleep-active neurons within the rostral medullary brainstem.

Textile Inspired Lithium–Oxygen Battery Cathode with Decoupled Oxygen and Electrolyte Pathways

Abstract: The lithium-air (Li-O2 ) battery has been deemed one of the most promising next-generation energy-storage devices due to its ultrahigh energy density. However, in conventional porous carbon-air cathodes, the oxygen gas and electrolyte often compete for transport pathways, which limit battery performance. Here, a novel textile-based air cathode is developed with a triple-phase structure to improve overall battery performance. The hierarchical structure of the conductive textile network leads to decoupled pathways for oxygen gas and electrolyte: oxygen flows through the woven mesh while the electrolyte diffuses along the textile fibers. Due to noncompetitive transport, the textile-based Li-O2 cathode exhibits a high discharge capacity of 8.6 mAh cm-2 , a low overpotential of 1.15 V, and stable operation exceeding 50 cycles. The textile-based structure can be applied to a range of applications (fuel cells, water splitting, and redox flow batteries) that involve multiple phase reactions. The reported decoupled transport pathway design also spurs potential toward flexible/wearable Li-O2 batteries.

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

Abstract: Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.