Nutrient availability is one of the strongest determinants of cell size. When grown in rich media, single-celled organisms such as yeast and bacteria can be up to twice the size of their slow-growing counterparts. The ability to modulate size in a nutrient-dependent manner requires cells to: (1) detect when they have reached the appropriate mass for a given growth rate and (2) transmit this information to the division apparatus. We report the identification of a metabolic sensor that couples nutritional availability to division in Bacillus subtilis. A key component of this sensor is an effector, UgtP, which localizes to the division site in a nutrient-dependent manner and inhibits assembly of the tubulin-like cell division protein FtsZ. This sensor serves to maintain a constant ratio of FtsZ rings to cell length regardless of growth rate and ensures that cells reach the appropriate mass and complete chromosome segregation prior to cytokinesis.

Plant and animal pathogenic bacteria can suppress host immunity by injecting type III secreted effector (T3SE) proteins into host cells. However, T3SEs can also elicit host immunity if the host has evolved a means to recognize the presence or activity of specific T3SEs. The diverse YopJ/HopZ/AvrRxv T3SE superfamily, which is found in both animal and plant pathogens, provides examples of T3SEs playing this dual role. The T3SE HopZ1a is an acetyltransferase carried by the phytopathogen Pseudomonas syringae that elicits effector-triggered immunity (ETI) when recognized in Arabidopsis thaliana by the nucleotide-binding leucine-rich repeat (NB-LRR) protein ZAR1. However, recognition of HopZ1a does not require any known ETI-related genes. Using a forward genetics approach, we identify a unique ETI-associated gene that is essential for ZAR1-mediated immunity. The hopZ-ETI-deficient1 (zed1) mutant is specifically impaired in the recognition of HopZ1a, but not the recognition of other unrelated T3SEs or in pattern recognition receptor (PRR)-triggered immunity. ZED1 directly interacts with both HopZ1a and ZAR1 and is acetylated on threonines 125 and 177 by HopZ1a. ZED1 is a nonfunctional kinase that forms part of small genomic cluster of kinases in Arabidopsis. We hypothesize that ZED1 acts as a decoy to lure HopZ1a to the ZAR1-resistance complex, resulting in ETI activation.ZED1-related kinase | ZRK | hypersensitive response

The eukaryotic cytoskeleton is essential for structural support and intracellular transport, and is therefore a common target of animal pathogens. However, no phytopathogenic effector has yet been demonstrated to specifically target the plant cytoskeleton. Here we show that the Pseudomonas syringae type III secreted effector HopZ1a interacts with tubulin and polymerized microtubules. We demonstrate that HopZ1a is an acetyltransferase activated by the eukaryotic co-factor phytic acid. Activated HopZ1a acetylates itself and tubulin. The conserved autoacetylation site of the YopJ / HopZ superfamily, K289, plays a critical role in both the avirulence and virulence function of HopZ1a. Furthermore, HopZ1a requires its acetyltransferase activity to cause a dramatic decrease in Arabidopsis thaliana microtubule networks, disrupt the plant secretory pathway and suppress cell wall-mediated defense. Together, this study supports the hypothesis that HopZ1a promotes virulence through cytoskeletal and secretory disruption.

Systems biology can unravel complex biology but has not been extensively applied to human newborns, a group highly vulnerable to a wide range of diseases. We optimized methods to extract transcriptomic, proteomic, metabolomic, cytokine/chemokine, and single cell immune phenotyping data from <1 ml of blood, a volume readily obtained from newborns. Indexing to baseline and applying innovative integrative computational methods reveals dramatic changes along a remarkably stable developmental trajectory over the first week of life. This is most evident in changes of interferon and complement pathways, as well as neutrophil-associated signaling. Validated across two independent cohorts of newborns from West Africa and Australasia, a robust and common trajectory emerges, suggesting a purposeful rather than random developmental path. Systems biology and innovative data integration can provide fresh insights into the molecular ontogeny of the first week of life, a dynamic developmental phase that is key for health and disease.

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A B S T R A C T PurposeSeveral mechanisms have been proposed to explain tamoxifen resistance of estrogen receptor (ER) -positive tumors, but a clinically useful explanation for such resistance has not been described. Because the ER is the treatment target for tamoxifen, a linear association between ER expression levels and the degree of benefit from tamoxifen might be expected. However, such an association has never been demonstrated with conventional clinical ER assays, and the ER is currently used clinically as a dichotomous marker. We used gene expression profiling and ER protein assays to help elucidate molecular mechanism(s) responsible for tamoxifen resistance in breast tumors. Patients and MethodsWe performed gene expression profiling of paraffin-embedded tumors from National Surgical Adjuvant Breast and Bowel Project (NSABP) trials that tested the worth of tamoxifen as an adjuvant systemic therapy (B-14) and as a preventive agent (P-1). This was a retrospective subset analysis based on available materials. ResultsIn B-14, ESR1 was the strongest linear predictor of tamoxifen benefit among 16 genes examined, including PGR and ERBB2. On the basis of these data, we hypothesized that, in the P-1 trial, a lower level of ESR1 mRNA in the tamoxifen arm was the main difference between the two study arms. Only ESR1 was downregulated by more than two-fold in ER-positive cancer events in the tamoxifen arm (P Ͻ .001). Tamoxifen did not prevent ER-positive tumors with low levels of ESR1 expression. ConclusionThese data suggest that low-level expression of ESR1 is a determinant of tamoxifen resistance in ER-positive breast cancer. Strategies should be developed to identify, treat, and prevent such tumors.

Background Inter-individual variability during sepsis limits appropriate triage of patients. Identifying, at first clinical presentation, gene expression signatures that predict subsequent severity will allow clinicians to identify the most at-risk groups of patients and enable appropriate antibiotic use.Methods Blood RNA-Seq and clinical data were collected from 348 patients in four emergency rooms (ER) and one intensive-care-unit (ICU), and 44 healthy controls. Gene expression profiles were analyzed using machine learning and data mining to identify clinically relevant gene signatures reflecting disease severity, organ dysfunction, mortality, and specific endotypes/mechanisms. Findings Gene expression signatures were obtained that predicted severity/organ dysfunction and mortality in both ER and ICU patients with accuracy/AUC of 77À80%. Network analysis revealed these signatures formed a coherent biological program, with specific but overlapping mechanisms/pathways. Given the heterogeneity of sepsis, we asked if patients could be assorted into discrete groups with distinct mechanisms (endotypes) and varying severity. Patients with early sepsis could be stratified into five distinct and novel mechanistic endotypes, named Neutrophilic-Suppressive/NPS, Inflammatory/INF, Innate-Host-Defense/IHD, Interferon/IFN, and Adaptive/ADA, each based on »200 unique gene expression differences, and distinct pathways/mechanisms (e.g., IL6/STAT3 in NPS). Endotypes had varying overall severity with two severe (NPS/INF) and one relatively benign (ADA) groupings, consistent with reanalysis of previous endotype studies. A 40 gene-classification tool (accuracy=96%) and several gene-pairs (accuracy=89À97%) accurately predicted endotype status in both ER and ICU validation cohorts.Interpretation The severity and endotype signatures indicate that distinct immune signatures precede the onset of severe sepsis and lethality, providing a method to triage early sepsis patients.

We developed a gene expression-based predictive model for degree of benefit from trastuzumab and demonstrated that HER2-negative tumors belong to the moderate benefit group, thus providing justification for testing trastuzumab in HER2-negative patients (NSABP B-47).