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With the rise in antimicrobial resistance and the dearth of effective strategies to combat this threat, the development of novel therapies is of utmost importance. Targeting of bacterial signaling through their the two-component systems (TCSs) may be a viable strategy. TCSs are comprised of a sensory histidine kinase (HK), of which a bacterium can have up to 160 distinct proteins, and a cognate response regulator (RR). The TCSs are generally non-essential for life, but control many virulence and antibiotic-resistance mechanisms. This, along with their absence in animals makes the TCSs an attractive target for antimicrobial therapy, whether as a stand-alone treatments or adjuvants for existing therapies. This review focuses on progress in the development of inhibitors that target the HK ATP-binding domain. Because this domain is highly conserved, it may be feasible to disrupt multiple TCSs within a single organism to increase effectiveness and reduce pressure for the evolution of resistance., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Objectives: Based on our previous findings that postmenopausal women with estrone (E1) and estradiol (E2) concentrations at or above 1.3 pg/ml and 0.5 pg/ml, respectively, after 6 months of adjuvant anastrozole therapy had a three-fold risk of recurrence, we aimed to identify a single-nucleotide polymorphism (SNP)-based model that would predict elevated E1 and E2 and then validate it in an independent dataset., Patients and Methods: The test set consisted of 322 women from the M3 study and the validation set consisted of 152 patients from MA.27. All patients were treated with adjuvant anastrozole, had on-anastrozole E1 and E2 concentrations and genome-wide genotyping., Results: SNPs were identified from the M3 genome-wide association study. The best model to predict the E1-E2 phenotype with high balanced accuracy was a support vector machine model using clinical factors plus 46 SNPs. We did not have an independent cohort that is similar to the M3 study with clinical, E1-E2 phenotypes and genotype data to test our model. Hence, we chose a nested matched case-control cohort (MA.27 study) for testing. Our E1-E2 model was not validated but we found the MA.27 validation cohort was both clinically and genomically different., Conclusions: We identified a SNP-based model that had excellent performance characteristics for predicting the phenotype of elevated E1 and E2 in women treated with anastrozole. This model was not validated in an independent dataset but that dataset was clinically and genomically substantially different. The model will need validation in a prospective study.

Objective: To identify additional genetic variants beyond those observed in a previous genome-wide association study (GWAS) in women treated on the MA.27 clinical trial in which women were randomized to 5 years of adjuvant therapy with anastrozole or exemestane., Patients and Methods: We performed a matched case-control study in 234 women who had a recurrence of breast cancer (cases) and 649 women who had not (controls). The analysis was restricted to White women with an estrogen receptor-positive breast cancer. Multiplex PCR-based targeted deep sequencing was performed of the MIR2052HG region on chromosome 8 between positions 75.4 and 75.7, a span of 300 kb, in an attempt to identify additional functional single nucleotide polymorphisms (SNPs)., Results: A total of 4677 unique variants were identified that had not been identified in the previous GWAS. Clinical Annotation of Variants analysis revealed 10 variants, including eight SNPs and two insertion-deletion mutations with moderate or high impact. However, none of the common and variant regions was significant after adjustment for the most significant SNP (rs13260300) identified in our previous GWAS. We performed haplotype analysis that revealed two regions in which the haplotypes lost significance when adjusted for this prior GWAS SNP and one region with two significant haplotypes (P = 0.046 and 0.031) after adjusting for the GWAS SNP., Conclusion: We were unable to identify common or rare variant regions that added value to the findings from our previous GWAS. We did find two haplotypes that were significant after adjusting for our top GWAS SNP but these were considered to be of marginal value.

The 14- and 16-membered macrolide antibiotics are an important structural class. Ubiquitously produced by a number of bacterial strains, namely actinomycetes, purification and structure elucidation of the wide array of analogs is challenging, both for discovery efforts and methodologies to monitor for byproducts, metabolites, and contaminants. Collision-induced dissociation mass spectrometry offers an attractive solution, enabling characterization of mixtures, and providing a wealth of structural information. However, interpretation of these spectra can be difficult. We present a study of 14- and 16-membered macrolide antibiotics, including MS n analysis for unprecedented depth of coverage, and complimentary analysis with D 2 O and H 2 18 O labeling to elucidate fragmentation mechanisms. These analyses contrast the behaviors of varying classes of macrolides and highlight how analogues can be identified in relation to similar structures, which will provide utility for future studies of novel macrolides, as well as impurities, metabolites, and degradation products of pharmaceuticals. Graphical Abstract.

Lsr2 is a nucleoid-associated protein conserved throughout the actinobacteria, including the antibiotic-producing Streptomyces. Streptomyces species encode paralogous Lsr2 proteins (Lsr2 and Lsr2-like, or LsrL), and we show here that of the two, Lsr2 has greater functional significance. We found that Lsr2 binds AT-rich sequences throughout the chromosome, and broadly represses gene expression. Strikingly, specialized metabolic clusters were over-represented amongst its targets, and the cryptic nature of many of these clusters appears to stem from Lsr2-mediated repression. Manipulating Lsr2 activity in model species and uncharacterized isolates resulted in the production of new metabolites not seen in wild type strains. Our results suggest that the transcriptional silencing of biosynthetic clusters by Lsr2 may protect Streptomyces from the inappropriate expression of specialized metabolites, and provide global control over Streptomyces' arsenal of signaling and antagonistic compounds., Competing Interests: EG, XZ, SP, AJ, CR, SJ, H, SG, ST, SB, JH, EC, JN, ME No competing interests declared, (© 2019, Gehrke et al.)

ABPP methods have been utilized for the last two decades as a means to investigate complex proteomes in all three domains of life. Extensive use in eukaryotes has provided a more fundamental understanding of the biological processes involved in numerous diseases and has driven drug discovery and treatment campaigns. However, the use of ABPP in prokaryotes has been less common, although it has gained more attention over the last decade. The urgent need for understanding bacteriophysiology and bacterial pathogenicity at a foundational level has never been more apparent, as the rise in antibiotic resistance has resulted in the inadequate and ineffective treatment of infections. This is not only a result of resistance to clinically used antibiotics, but also a lack of new drugs and equally as important, new drug targets. ABPP provides a means for which new, clinically relevant drug targets may be identified through gaining insight into biological processes. In this chapter, we place particular focus on the discussion of ABPP strategies that have been applied to study different classes of bacterial enzymes.

Background: Estrone (E1), the major circulating estrogen in postmenopausal women, promotes estrogen-receptor positive (ER+) breast tumor growth and proliferation. Two major reactions contribute to E1 plasma concentrations, aromatase (CYP19A1) catalyzed E1 synthesis from androstenedione and steroid sulfatase (STS) catalyzed hydrolysis of estrone conjugates (E1Cs). E1Cs have been associated with breast cancer risk and may contribute to tumor progression since STS is expressed in breast cancer where its activity exceeds that of aromatase., Methods: We performed genome-wide association studies (GWAS) to identify SNPs associated with variation in plasma concentrations of E1Cs, E1, and androstenedione in 774 postmenopausal women with resected early-stage ER+ breast cancer. Hormone concentrations were measured prior to aromatase inhibitor therapy., Results: Multiple SNPs in SLCO1B1, a gene encoding a hepatic influx transporter, displayed genome-wide significant associations with E1C plasma concentrations and with the E1C/E1 ratio. The top SNP for E1C concentrations, rs4149056 (p = 3.74E-11), was a missense variant that results in reduced transporter activity. Patients homozygous for the variant allele had significantly higher average E1C plasma concentrations than did other patients. Furthermore, three other SLCO1B1 SNPs, not in LD with rs4149056, were associated with both E1C concentrations and the E1C/E1 ratio and were cis-eQTLs for SLCO1B3. GWAS signals of suggestive significance were also observed for E1, androstenedione, and the E1/androstenedione ratio., Conclusion: These results suggest a mechanism for genetic variation in E1C plasma concentrations as well as possible SNP biomarkers to identify ER+ breast cancer patients for whom STS inhibitors might be of clinical value.

The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry (MS) techniques are well-suited to high-throughput characterization of NP, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social Molecular Networking (GNPS; http://gnps.ucsd.edu), an open-access knowledge base for community-wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS, crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of 'living data' through continuous reanalysis of deposited data.

The adenosine triphosphate (ATP) analogue adenosine 5'-O-(3-thiotriphosphate) (ATPγS) has been applied as a tool to study kinase-substrate phosphorylation. Not only does the transfer of a thiophosphate group represent a unique modification amid the phosphoproteome, but it can also be stable to phosphatase activity. However, detection of this species is complicated due to the similar chemical reactivity of thiophosphate and proteinaceous thiols. Here, we describe a novel method for detection of protein thiophosphorylation utilizing the thiol-ene reaction. By first chemoselectively capping protein thiols through radical chemistry, kinase-catalyzed thiophosphorylation can be visualized specifically.

Siderophores are bacterially secreted, small molecule iron chelators that facilitate the binding of insoluble iron (III) for reuptake and use in various biological processes. These compounds are classified by their iron (III) binding geometry, as dictated by subunit composition and include groups such as the trihydroxamates (hexadentate ligand) and catecholates (bidentate). Small modifications to the core structure such as acetylation, lipid tail addition, or cyclization, make facile characterization of new siderophores difficult by molecular ion detection alone (MS(1)). We have expanded upon previous fragmentation-directed studies using electrospray ionization collision-induced dissociation tandem mass spectrometry (ESI-CID-MS/MS/MS) and identified diagnostic MS(3) features from the trihydroxamate siderophore class for ferrioxamine B and E1 by accurate mass. Diagnostic features for MS(3) include C-C, C-N, amide, and oxime cleavage events with proposed losses of water and -CO from the iron (III) coordination sites. These insights will facilitate the discovery of novel trihydroxamate siderophores from complex sample matrices. Graphical Abstract ᅟ.

Secondary metabolite discovery from bacteria has become increasingly successful in the last decade due to the advancement of integrated genetic-based, spectrometric-based and informatics-based techniques. Microbes and their unique metabolic outputs have been widely studied since the beginning of modern medicine; however, it is well known that the current repertoire of secondary metabolites, or more commonly natural products, is incomplete and the understanding of natural product-mediated intracellular dialog is in its infancy. Here, we highlight the present state of bacterial metabolomics including compound discovery approaches and new strategies for probing the role of these molecules within communication networks., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Recent genetic studies have identified common variation in susceptibility loci that stratify lifetime risks of breast cancer and may inform prevention and screening strategies. However, whether these loci have similar implications for women treated with tamoxifen or raloxifene (SERMs) is unknown. We conducted a matched case-control study of 592 cases who developed breast cancer and 1,171 unaffected women from 32,859 participants on SERM therapy enrolled on NSABP P-1 and P-2 breast cancer prevention trials. We formed a quantitative polygenic risk score (PRS) using genotypes of 75 breast cancer-associated single nucleotide polymorphisms and examined the PRS as a risk factor for breast cancer among women treated with SERMs. The PRS ranged from 3.98 to 7.74, with a one-unit change associated with a 42 % increase in breast cancer (OR = 1.42; P = 0.0002). The PRS had a stronger association with breast cancer among high-risk women with no first-degree family history (OR = 1.62) compared to those with a positive family history (OR = 1.32) (P intx = 0.04). There was also suggestion that PRS was a stronger risk factor for ER-positive (OR = 1.59, P = 0.0002) than ER-negative (OR = 1.05, P = 0.84) breast cancer (P intx = 0.10). Associations did not differ by tamoxifen or raloxifene treatment, age at trial entry, 5-year predicted Gail model risk or other clinical variables. The PRS is a strong risk factor for ER-positive breast cancer in moderate to high-risk individuals treated with either tamoxifen or raloxifene for cancer prevention. These data suggest that common genetic variation informs risk of breast cancer in women receiving SERMs.

Bone fractures are a major consequence of osteoporosis. There is a direct relationship between serum estrogen concentrations and osteoporosis risk. Aromatase inhibitors (AIs) greatly decrease serum estrogen levels in postmenopausal women, and increased incidence of fractures is a side effect of AI therapy. We performed a discovery case-cohort genome-wide association study (GWAS) using samples from 1071 patients, 231 cases and 840 controls, enrolled in the MA.27 breast cancer AI trial to identify genetic factors involved in AI-related fractures, followed by functional genomic validation. Association analyses identified 20 GWAS single nucleotide polymorphism (SNP) signals with P < 5E-06. After removal of signals in gene deserts and those composed entirely of imputed SNPs, we applied a functional validation "decision cascade" that resulted in validation of the CTSZ-SLMO2-ATP5E, TRAM2-TMEM14A, and MAP4K4 genes. These genes all displayed estradiol (E2)-dependent induction in human fetal osteoblasts transfected with estrogen receptor-α, and their knockdown altered the expression of known osteoporosis-related genes. These same genes also displayed SNP-dependent variation in E2 induction that paralleled the SNP-dependent induction of known osteoporosis genes, such as osteoprotegerin. In summary, our case-cohort GWAS identified SNPs in or near CTSZ-SLMO2-ATP5E, TRAM2-TMEM14A, and MAP4K4 that were associated with risk for bone fracture in estrogen receptor-positive breast cancer patients treated with AIs. These genes displayed E2-dependent induction, their knockdown altered the expression of genes related to osteoporosis, and they displayed SNP genotype-dependent variation in E2 induction. These observations may lead to the identification of novel mechanisms associated with fracture risk in postmenopausal women treated with AIs.