Genome mining has become a key technology to exploit natural product diversity. While initially performed on a single-genome basis, the process is now being scaled up to mine entire genera, strain collections and microbiomes. However, no bioinformatic framework is currently available for effectively analyzing datasets of this size and complexity. Here, we provide a streamlined computational workflow consisting of two new software tools: The 'Biosynthetic Gene Similarity Clustering And Prospecting Engine' (BiG-SCAPE) facilitates fast and interactive sequence similarity network analysis of biosynthetic gene clusters and gene cluster families. 'CORe Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:

Agents, such as β-lapachone, that target the redox enzyme, NAD(P)H:quinone oxidoreductase 1 (NQO1), to induce programmed necrosis in solid tumors have shown great promise, but more potent tumor-selective compounds are needed. Here, we report that deoxynyboquinone kills a wide spectrum of cancer cells in an NQO1-dependent manner with greater potency than β-lapachone. Deoxynyboquinone lethality relies on NQO1-dependent futile redox cycling that consumes oxygen and generates extensive reactive oxygen species (ROS). Elevated ROS levels cause extensive DNA lesions, PARP1 hyperactivation, and severe NAD+/ATP depletion that stimulate Ca2+–dependent programmed necrosis, unique to this new class of NQO1 "bioactivated" drugs. Short-term exposure of NQO1+ cells to deoxynyboquinone was sufficient to trigger cell death, although genetically matched NQO1− cells were unaffected. Moreover, siRNA-mediated NQO1 or PARP1 knockdown spared NQO1+ cells from short-term lethality. Pretreatment of cells with BAPTA-AM (a cytosolic Ca2+ chelator) or catalase (enzymatic H2O2 scavenger) was sufficient to rescue deoxynyboquinone-induced lethality, as noted with β-lapachone. Investigations in vivo showed equivalent antitumor efficacy of deoxynyboquinone to β-lapachone, but at a 6-fold greater potency. PARP1 hyperactivation and dramatic ATP loss were noted in the tumor, but not in the associated normal lung tissue. Our findings offer preclinical proof-of-concept for deoxynyboquinone as a potent chemotherapeutic agent for treatment of a wide spectrum of therapeutically challenging solid tumors, such as pancreatic and lung cancers.

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To determine whether premenopausal daughters of women with postmenopausal osteoporosis have lower bone mass than other women of the same age, we measured the bone mineral content of the lumbar spine and femoral neck and midshaft, using dual-photon absorptiometry, in 25 postmenopausal women with osteoporotic compression fractures and in 32 of their premenopausal daughters; we then compared the results with those in normal controls. As compared with normal postmenopausal women, women with osteoporosis had lower bone mineral content in the lumbar spine, femoral neck, and femoral midshaft by 33, 24, and 15 percent, respectively (P less than 0.001 for each comparison by the one-tailed t-test). As compared with normal premenopausal women, the daughters of women with osteoporosis had lower bone mineral content at these sites by 7, 5, and 3 percent, respectively (P = 0.03, 0.07, and 0.15, respectively, by the one-tailed t-test). In terms of a standardized score, we calculated that the mean (+/- SEM) relative deficits in bone mineral content in the daughters of women with osteoporosis were 58 +/- 18 percent (lumbar spine) and 34 +/- 16 percent (femoral neck) of the relative deficits in their mothers. We conclude that daughters of women with osteoporosis have reduced bone mass in the lumbar spine and perhaps in the femoral neck; this reduction in bone mass may put them at increased risk for fractures. We also conclude that postmenopausal osteoporosis may result partly from a relatively low peak bone mass rather than from excessive loss of bone.

A major goal of personalized medicine in oncology is the identification of drugs with predictable efficacy based on a specific trait of the cancer cell, as has been demonstrated with gleevec (presence of Bcr-Abl protein), herceptin (Her2 overexpression), and iressa (presence of a specific EGFR mutation). This is a challenging task, as it requires identifying a cellular component that is altered in cancer, but not normal cells, and discovering a compound that specifically interacts with it. The enzyme NQO1 is a potential target for personalized medicine, as it is overexpressed in many solid tumors. In normal cells NQO1 is inducibly expressed, and its major role is to detoxify quinones via bioreduction; however, certain quinones become more toxic after reduction by NQO1, and these compounds have potential as selective anticancer agents. Several quinones of this type have been reported, including mitomycin C, RH1, EO9, streptonigrin, β-lapachone, and deoxynyboquinone (DNQ). However, no unified picture has emerged from these studies, and the key question regarding the relationship between NQO1 processing and anticancer activity remains unanswered. Here, we directly compare these quinones as substrates for NQO1 in vitro, and for their ability to kill cancer cells in culture in an NQO1-dependent manner. We show that DNQ is a superior NQO1 substrate, and we use computationally guided design to create DNQ analogues that have a spectrum of activities with NQO1. Assessment of these compounds definitively establishes a strong relationship between in vitro NQO1 processing and induction of cancer cell death and suggests these compounds are outstanding candidates for selective anticancer therapy.

Genome mining has become a key technology to explore and exploit natural product diversity through the identification and analysis of biosynthetic gene clusters (BGCs). Initially, this was performed on a single-genome basis; currently, the process is being scaled up to large-scale mining of pan-genomes of entire genera, complete strain collections and metagenomic datasets from which thousands of bacterial genomes can be extracted at once. However, no bioinformatic framework is currently available for the effective analysis of datasets of this size and complexity. Here, we provide a streamlined computational workflow, tightly integrated with antiSMASH and MIBiG, that consists of two new software tools, BiG-SCAPE and CORASON. BiG-SCAPE facilitates rapid calculation and interactive visual exploration of BGC sequence similarity networks, grouping gene clusters at multiple hierarchical levels, and includes a 'glocal' alignment mode that accurately groups both complete and fragmented BGCs. CORASON employs a phylogenomic approach to elucidate the detailed evolutionary relationships between gene clusters by computing high-resolution multi-locus phylogenies of all BGCs within and across gene cluster families (GCFs), and allows researchers to comprehensively identify all genomic contexts in which particular biosynthetic gene cassettes are found. We validate BiG-SCAPE by correlating its GCF output to metabolomic data across 403 actinobacterial strains. Furthermore, we demonstrate the discovery potential of the platform by using CORASON to comprehensively map the phylogenetic diversity of the large detoxin/rimosamide gene cluster clan, prioritizing three new detoxin families for subsequent characterization of six new analogs using isotopic labeling and analysis of tandem mass spectrometric data.

One of the major goals of cancer therapy is the selective targeting of cancer cells over normal cells. Unfortunately, even with recent advances, the majority of chemotherapeutics still indiscriminately kill all rapidly dividing cells. Although these drugs are effective in certain settings, their inability to specifically target cancer results in significant dose-limiting toxicities. One way to avoid such toxicities is to target an aspect of the cancer cell that is not shared by normal cells. A potential cancer-specific target is the enzyme NAD(P)H quinone oxidoreductase 1 (NQO1). NQO1 is a 2-electron reductase responsible for the detoxification of quinones. Its expression is typically quite low in normal tissue, but it has been found to be greatly overexpressed in many types of solid tumors, including lung, breast, pancreatic, and colon cancers. This overexpression is thought to be in response to the higher oxidative stress of the cancer cell, and it is possible that NQO1 contributes to tumor progression. The overexpression of NQO1 and its correlation with poor patient outcome make it an intriguing target. Although some have explored inhibiting NQO1 as an anticancer strategy, this has generally been unsuccessful. A more promising strategy is to utilize NQO1 substrates that are activated upon reduction by NQO1. For example, in principle, reduction of a quinone can result in a hydroquinone that is a DNA alkylator, protein inhibitor, or reduction-oxidation cycler. Although there are many proposed NQO1 substrates, head-to-head assays reveal only two classes of compounds that convincingly induce cancer cell death through NQO1-mediated activation. In this Account, we describe the discovery and development of one of these compounds, the natural product deoxynyboquinone (DNQ), an excellent NQO1 substrate and anticancer agent. A modular synthesis of DNQ was developed that enabled access to the large compound quantities needed to conduct extensive mechanistic evaluations and animal experiments. During these evaluations, we found that DNQ is an outstanding NQO1 substrate that is processed much more efficiently than other putative NQO1 substrates. Importantly, its anticancer activity is strictly dependent on the overexpression of active NQO1. Using previous crystal structures of NQO1, novel DNQ derivatives were designed that are also excellent NQO1 substrates and possess properties that make them more attractive than the parent natural product for translational development. Given their selectivity, potency, outstanding pharmacokinetic properties, and the ready availability of diagnostics to assess NQO1 in patients, DNQ and its derivatives have considerable potential as personalized medicines for the treatment of cancer.

Genomics and metabolomics are widely used to explore specialized metabolite diversity. The Paired Omics Data Platform is a community initiative to systematically document links between metabolome and (meta)genome data, aiding identification of natural product biosynthetic origins and metabolite structures.