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Zelzion, E.

Publications and source records attributed to Zelzion, E..

2 recordsLinked to original sources

The Staphylococcus aureus small non-coding RNA IsrR regulates TCA cycle activity and virulence

Staphylococcus aureus has evolved mechanisms to cope with low iron (Fe) availability in host tissues. S. aureus uses the ferric uptake transcriptional regulator (Fur) to sense titers of cytosolic Fe. Upon Fe depletion, apo-Fur relieves transcriptional repression of genes utilized for Fe uptake. We demonstrate that an S. aureus {Delta}fur mutant has decreased expression of acnA, which codes for the Fe-dependent enzyme aconitase. Decreased acnA expression prevented the {Delta}fur mutant from growing with amino acids as sole carbon and energy sources. Suppressor analysis determined that a mutation in isrR, which produces a regulatory RNA, permitted growth by decreasing isrR transcription. The decreased AcnA activity of the {Delta}fur mutant was partially relieved by an {Delta}isrR mutation. Directed mutation of bases predicted to facilitate the interaction between the acnA transcript and IsrR, decreased the ability of IsrR to control acnA expression in vivo and IsrR bound to the acnA transcript in vitro. IsrR also bound to the transcripts coding the alternate TCA cycle proteins sdhC, mqo, citZ, and citM. Whole cell metal analyses suggest that IsrR promotes Fe uptake and increases intracellular Fe not ligated by macromolecules. Lastly, we determined that Fur and IsrR promote infection using murine skin and acute pneumonia models. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/601953v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@1ec27b2org.highwire.dtl.DTLVardef@1224b64org.highwire.dtl.DTLVardef@83cd49org.highwire.dtl.DTLVardef@11a9cea_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

Outcome of Crash Course Training on Protein Structure Prediction with Artificial Intelligence

Protein structure predictions have broad impact on several science disciplines such as biology, bioengineering, and medical science. AlphaFold2[1] and RoseTTAFold[2] are the current state-of-the-art AI methods to predict the structures of proteins with an accuracy comparable to lower-resolution experimental methods. In its 2021 year review, both these methods were recognized as "breakthrough of the year" by Science magazine[3] and "method of the year" by Nature magazine [4]. It is timely and important to provide training and support on these emerging methods. Our crash course "Enabling Protein Structure Prediction with Artificial Intelligence "was conducted in collaboration with domain experts and research computing professionals. The crash course was well received by the community as there were 750 registrants from all over the world. Here we provide the summary of the crash course, describe our findings in organizing the crash course, and explain what preparation steps helped us with the hands-on training. CCS CONCEPTSComputing methodologies a Machine learning a Machine learning approaches a Bio-inspired approaches

scientific communication and education↗