Distinct Chemotaxis Protein Paralogs Assemble into Chemoreceptor Signaling Arrays To Coordinate Signaling Output

Most chemotactic motile bacteria possess multiple chemotaxis signaling systems, the functions of which are not well characterized. Chemotaxis signaling is initiated by chemoreceptors that assemble as large arrays, together with chemotaxis coupling proteins (CheW) and histidine kinase proteins (CheA), which form a baseplate with the cytoplasmic tips of receptors. These cell pole-localized arrays mediate sensing, signaling, and signal amplification during chemotaxis responses. Membrane-bound chemoreceptors with different cytoplasmic domain lengths segregate into distinct arrays. Here, we show that a bacterium, Azospirillum brasilense, which utilizes two chemotaxis signaling systems controlling distinct motility parameters, coordinates its chemotactic responses through the production of two separate membrane-bound chemoreceptor arrays by mixing paralogs within chemotaxis baseplates. The polar localization of chemoreceptors of different length classes is maintained in strains that had baseplate signaling proteins from either chemotaxis system but was lost when both systems were deleted. Chemotaxis proteins (CheA and CheW) from each of the chemotaxis signaling systems (Che1 and Che4) could physically interact with one another, and chemoreceptors from both classes present in A. brasilense could interact with Che1 and Che4 proteins. The assembly of paralogs from distinct chemotaxis pathways into baseplates provides a straightforward mechanism for coordinating signaling from distinct pathways, which we predict is not unique to this system given the propensity of chemotaxis systems for horizontal gene transfer

Distinct Chemotaxis Protein Paralogs Assemble into Chemoreceptor Signaling Arrays To Coordinate Signaling Output

Most chemotactic motile bacteria possess multiple chemotaxis signaling systems, the functions of which are not well characterized. Chemotaxis signaling is initiated by chemoreceptors that assemble as large arrays, together with chemotaxis coupling proteins (CheW) and histidine kinase proteins (CheA), which form a base- plate with the cytoplasmic tips of receptors. These cell pole-localized arrays mediate sensing, signaling, and signal amplification during chemotaxis responses. Membrane- bound chemoreceptors with different cytoplasmic domain lengths segregate into distinct arrays. Here, we show that a bacterium, Azospirillum brasilense, which utilizes two chemotaxis signaling systems controlling distinct motility parameters, coordi- nates its chemotactic responses through the production of two separate membrane- bound chemoreceptor arrays by mixing paralogs within chemotaxis baseplates. The polar localization of chemoreceptors of different length classes is maintained in strains that had baseplate signaling proteins from either chemotaxis system but was lost when both systems were deleted. Chemotaxis proteins (CheA and CheW) from each of the chemotaxis signaling systems (Che1 and Che4) could physically interact with one another, and chemoreceptors from both classes present in A. brasilense could interact with Che1 and Che4 proteins. The assembly of paralogs from distinct chemotaxis pathways into baseplates provides a straightforward mechanism for co- ordinating signaling from distinct pathways, which we predict is not unique to this system given the propensity of chemotaxis systems for horizontal gene transfer

It’s All in the PAN: Crosstalk, Plasticity, Redundancies, Switches, and Interconnectedness Encompassed by PANoptosis Underlying the Totality of Cell Death-Associated Biological Effects

The innate immune system provides the first line of defense against cellular perturbations. Innate immune activation elicits inflammatory programmed cell death in response to microbial infections or alterations in cellular homeostasis. Among the most well-characterized programmed cell death pathways are pyroptosis, apoptosis, and necroptosis. While these pathways have historically been defined as segregated and independent processes, mounting evidence shows significant crosstalk among them. These molecular interactions have been described as ‘crosstalk’, ‘plasticity’, ‘redundancies’, ‘molecular switches’, and more. Here, we discuss the key components of cell death pathways and note several examples of crosstalk. We then explain how the diverse descriptions of crosstalk throughout the literature can be interpreted through the lens of an integrated inflammatory cell death concept, PANoptosis. The totality of biological effects in PANoptosis cannot be individually accounted for by pyroptosis, apoptosis, or necroptosis alone. We also discuss PANoptosomes, which are multifaceted macromolecular complexes that regulate PANoptosis. We consider the evidence for PANoptosis, which has been mechanistically characterized during influenza A virus, herpes simplex virus 1, Francisella novicida, and Yersinia infections, as well as in response to altered cellular homeostasis, in inflammatory diseases, and in cancers. We further discuss the role of IRF1 as an upstream regulator of PANoptosis and conclude by reexamining historical studies which lend credence to the PANoptosis concept. Cell death has been shown to play a critical role in infections, inflammatory diseases, neurodegenerative diseases, cancers, and more; therefore, having a holistic understanding of cell death is important for identifying new therapeutic strategies

It’s All in the PAN: Crosstalk, Plasticity, Redundancies, Switches, and Interconnectedness Encompassed by PANoptosis Underlying the Totality of Cell Death-Associated Biological Effects

The innate immune system provides the first line of defense against cellular perturbations. Innate immune activation elicits inflammatory programmed cell death in response to microbial infections or alterations in cellular homeostasis. Among the most well-characterized programmed cell death pathways are pyroptosis, apoptosis, and necroptosis. While these pathways have historically been defined as segregated and independent processes, mounting evidence shows significant crosstalk among them. These molecular interactions have been described as ‘crosstalk’, ‘plasticity’, ‘redundancies’, ‘molecular switches’, and more. Here, we discuss the key components of cell death pathways and note several examples of crosstalk. We then explain how the diverse descriptions of crosstalk throughout the literature can be interpreted through the lens of an integrated inflammatory cell death concept, PANoptosis. The totality of biological effects in PANoptosis cannot be individually accounted for by pyroptosis, apoptosis, or necroptosis alone. We also discuss PANoptosomes, which are multifaceted macromolecular complexes that regulate PANoptosis. We consider the evidence for PANoptosis, which has been mechanistically characterized during influenza A virus, herpes simplex virus 1, Francisella novicida, and Yersinia infections, as well as in response to altered cellular homeostasis, in inflammatory diseases, and in cancers. We further discuss the role of IRF1 as an upstream regulator of PANoptosis and conclude by reexamining historical studies which lend credence to the PANoptosis concept. Cell death has been shown to play a critical role in infections, inflammatory diseases, neurodegenerative diseases, cancers, and more; therefore, having a holistic understanding of cell death is important for identifying new therapeutic strategies

Host Fatty Acid Utilization by Staphylococcus aureus at the Infection Site

The shortage of antibiotics against drug-resistant Staphylococcus aureus has led to the development of new drugs targeting the elongation cycle of fatty acid (FA) synthesis that are progressing toward the clinic. An objection to the use of FA synthesis inhibitors is that S. aureus can utilize exogenous FAs to construct its membrane, suggesting that the bacterium would bypass these therapeutics by utilizing host FAs instead. We developed a mass spectrometry workflow to determine the composition of the S. aureus membrane at the infection site to directly address how S. aureus uses host FAs. S. aureus strains that cannot acquire host FAs are as effective in establishing an infection as the wild type, but strains that require the utilization of host FAs for growth were attenuated in the mouse thigh infection model. We find that S. aureus does utilize host FAs to construct its membrane, but host FAs do not replace the requirement for pentadecanoic acid, a branched-chain FA derived from isoleucine (or leucine) that predominantly occupies the 2 position of S. aureus phospholipids. The membrane phospholipid structure of S. aureus mutants that cannot utilize host FAs indicates the isoleucine is a scarce resource at the infection site. This reliance on the de novo synthesis of predominantly pentadecanoic acid that cannot be obtained from the host is one reason why drugs that target fatty acid synthesis are effective in treating S. aureus infections.Staphylococcus aureus utilizes the fatty acid (FA) kinase system to activate exogenous FAs for membrane synthesis. We developed a lipidomics workflow to determine the membrane phosphatidylglycerol (PG) molecular species synthesized by S. aureus at the thigh infection site. Wild-type S. aureus utilizes both host palmitate and oleate to acylate the 1 position of PG, and the 2 position is occupied by pentadecanoic acid arising from de novo biosynthesis. Inactivation of FakB2 eliminates the ability to assimilate oleate and inactivation of FakB1 reduces the content of saturated FAs and enhances oleate utilization. Elimination of FA activation in either ΔfakA or ΔfakB1 ΔfakB2 mutants does not impact growth. All S. aureus strains recovered from the thigh have significantly reduced branched-chain FAs and increased even-chain FAs compared to that with growth in rich laboratory medium. The molecular species pattern observed in the thigh was reproduced in the laboratory by growth in isoleucine-deficient medium containing exogenous FAs. S. aureus utilizes specific host FAs for membrane biosynthesis but also requires de novo FA biosynthesis initiated by isoleucine (or leucine) to produce pentadecanoic acid

Distinct Chemotaxis Protein Paralogs Assemble into Chemoreceptor Signaling Arrays To Coordinate Signaling Output

The assembly of chemotaxis receptors and signaling proteins into polar arrays is universal in motile chemotactic bacteria. Comparative genome analyses indicate that most motile bacteria possess multiple chemotaxis signaling systems, and experimental evidence suggests that signaling from distinct chemotaxis systems is integrated. Here, we identify one such mechanism. We show that paralogs from two chemotaxis systems assemble together into chemoreceptor arrays, forming baseplates comprised of proteins from both chemotaxis systems. These mixed arrays provide a straightforward mechanism for signal integration and coordinated response output from distinct chemotaxis systems. Given that most chemotactic bacteria encode multiple chemotaxis systems and the propensity for these systems to be laterally transferred, this mechanism may be common to ensure chemotaxis signal integration occurs.Most chemotactic motile bacteria possess multiple chemotaxis signaling systems, the functions of which are not well characterized. Chemotaxis signaling is initiated by chemoreceptors that assemble as large arrays, together with chemotaxis coupling proteins (CheW) and histidine kinase proteins (CheA), which form a baseplate with the cytoplasmic tips of receptors. These cell pole-localized arrays mediate sensing, signaling, and signal amplification during chemotaxis responses. Membrane-bound chemoreceptors with different cytoplasmic domain lengths segregate into distinct arrays. Here, we show that a bacterium, Azospirillum brasilense, which utilizes two chemotaxis signaling systems controlling distinct motility parameters, coordinates its chemotactic responses through the production of two separate membrane-bound chemoreceptor arrays by mixing paralogs within chemotaxis baseplates. The polar localization of chemoreceptors of different length classes is maintained in strains that had baseplate signaling proteins from either chemotaxis system but was lost when both systems were deleted. Chemotaxis proteins (CheA and CheW) from each of the chemotaxis signaling systems (Che1 and Che4) could physically interact with one another, and chemoreceptors from both classes present in A. brasilense could interact with Che1 and Che4 proteins. The assembly of paralogs from distinct chemotaxis pathways into baseplates provides a straightforward mechanism for coordinating signaling from distinct pathways, which we predict is not unique to this system given the propensity of chemotaxis systems for horizontal gene transfer

Rationale and Design for a GRADE Substudy of Continuous Glucose Monitoring

Background: The Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness (GRADE) study has enrolled a racially and ethnically diverse population with type 2 diabetes, performed extensive phenotyping, and randomly assigned the participants to one of four second-line diabetes medications. The continuous glucose monitoring (CGM) substudy has been added to determine whether there are racial/ethnic differences in the relationship between average glucose (AG) and hemoglobin A1c (HbA1c). CGM will also be used to compare time in target range, glucose variability, and the frequency and duration of hypoglycemia across study groups. Methods: The observational CGM substudy will enroll up to 1800 of the 5047 GRADE study participants from the four treatment groups, including as many as 450 participants from each of 4 racial/ethnic minority groups to be compared: Hispanic White, non-Hispanic White, non-Hispanic African American, and non-Hispanic Other. CGM will be performed for 2 weeks in proximity to a GRADE annual visit, during which an oral glucose tolerance test will be performed and HbA1c and glycated albumin measured. Indicators of interindividual variation in red blood cell turnover, based on specialized erythrocyte measurements, will also be measured to explore the potential causes of interindividual HbA1c variations. Conclusions: The GRADE CGM substudy will provide new insights into whether differences exist in the relationship between HbA1c and AG among different racial/ethnic groups and whether glycemic profiles differ among frequently used diabetes medications and their potential clinical implications. Understanding such differences is important for clinical care and adjustment of diabetes medications in patients of different races or ethnicities

Association of Baseline Characteristics With Insulin Sensitivity and β-Cell Function in the Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness (GRADE) Study Cohort.

ObjectiveWe investigated sex and racial differences in insulin sensitivity, β-cell function, and glycated hemoglobin (HbA1c) and the associations with selected phenotypic characteristics.Research design and methodsThis is a cross-sectional analysis of baseline data from 3,108 GRADE (Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study) participants. All had type 2 diabetes diagnosed <10 years earlier and were on metformin monotherapy. Insulin sensitivity and β-cell function were evaluated using the HOMA of insulin sensitivity and estimates from oral glucose tolerance tests, including the Matsuda Index, insulinogenic index, C-peptide index, and oral disposition index (DI).ResultsThe cohort was 56.6 ± 10 years of age (mean ± SD), 63.8% male, with BMI 34.2 ± 6.7 kg/m2, HbA1c 7.5 ± 0.5%, and type 2 diabetes duration 4.0 ± 2.8 years. Women had higher DI than men but similar insulin sensitivity. DI was the highest in Black/African Americans, followed by American Indians/Alaska Natives, Asians, and Whites in descending order. Compared with Whites, American Indians/Alaska Natives had significantly higher HbA1c, but Black/African Americans and Asians had lower HbA1c. However, when adjusted for glucose levels, Black/African Americans had higher HbA1c than Whites. Insulin sensitivity correlated inversely with BMI, waist-to-hip ratio, triglyceride-to-HDL-cholesterol ratio (TG/HDL-C), and the presence of metabolic syndrome, whereas DI was associated directly with age and inversely with BMI, HbA1c, and TG/HDL-C.ConclusionsIn the GRADE cohort, β-cell function differed by sex and race and was associated with the concurrent level of HbA1c. HbA1c also differed among the races, but not by sex. Age, BMI, and TG/HDL-C were associated with multiple measures of β-cell function and insulin sensitivity

Optimization of Metformin in the GRADE Cohort: Effect on Glycemia and Body Weight

ObjectiveWe evaluated the effect of optimizing metformin dosing on glycemia and body weight in type 2 diabetes.Research design and methodsThis was a prespecified analysis of 6,823 participants in the Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study (GRADE) taking metformin as the sole glucose-lowering drug who completed a 4- to 14-week (mean ± SD 7.9 ± 2.4) run-in in which metformin was adjusted to 2,000 mg/day or a maximally tolerated lower dose. Participants had type 2 diabetes for <10 years and an HbA1c ≥6.8% (51 mmol/mol) while taking ≥500 mg of metformin/day. Participants also received diet and exercise counseling. The primary outcome was the change in HbA1c during run-in.ResultsAdjusted for duration of run-in, the mean ± SD change in HbA1c was -0.65 ± 0.02% (-7.1 ± 0.2 mmol/mol) when the dose was increased by ≥1,000 mg/day, -0.48 ± 0.02% (-5.2 ± 0.2 mmol/mol) when the dose was unchanged, and -0.23 ± 0.07% (-2.5 ± 0.8 mmol/mol) when the dose was decreased (n = 2,169, 3,548, and 192, respectively). Higher HbA1c at entry predicted greater reduction in HbA1c (P < 0.001) in univariate and multivariate analyses. Weight loss adjusted for duration of run-in averaged 0.91 ± 0.05 kg in participants who increased metformin by ≥1,000 mg/day (n = 1,894).ConclusionsOptimizing metformin to 2,000 mg/day or a maximally tolerated lower dose combined with emphasis on medication adherence and lifestyle can improve glycemia in type 2 diabetes and HbA1c values ≥6.8% (51 mmol/mol). These findings may help guide efforts to optimize metformin therapy among persons with type 2 diabetes and suboptimal glycemic control