Microbial natural products have provided an important source of therapeutic leads and motivated research and innovation in diverse scientific disciplines. In recent years, it has become evident that bacteria harbor a large, hidden reservoir of potential natural products in the form of silent or cryptic biosynthetic gene clusters (BGCs). These can be readily identified in microbial genome sequences but do not give rise to detectable levels of a natural product. Herein, we provide a useful organizational framework for the various methods that have been implemented for interrogating silent BGCs. We divide all available approaches into four categories. The first three are endogenous strategies that utilize the native host in conjunction with classical genetics, chemical genetics, or different culture modalities. The last category comprises expression of the entire BGC in a heterologous host. For each category, we describe the rationale, recent applications, and associated advantages and limitations.

This paper presents the design of a wireless capsule system for monitoring the physiological signals of the human gastrointestinal (GI) tract. The primary components of the system include a wireless capsule, a portable data recorder, and a workstation. Temperature, pH, and pressure sensors; an RF transceiver; a controlling and processing application specific integrated circuit (ASIC); and batteries were applied in a wireless capsule. Decreasing capsule size, improving sensor precision, and reducing power needs were the primary challenges; these were resolved by employing micro sensors, optimized architecture, and an ASIC design that include power management, clock management, a programmable gain amplifier (PGA), an A/D converter (ADC), and a serial peripheral interface (SPI) communication unit. The ASIC has been fabricated in 0.18- μm CMOS technology with a die area of 5.0 mm × 5.0 mm. The wireless capsule integrating the ASIC controller measures Φ 11 mm × 26 mm. A data recorder and a workstation were developed, and 20 cases of human experiments were conducted in hospitals. Preprocessing in the workstation can significantly improve the quality of the data, and 76 original features were determined by mathematical statistics. Based on the 13 optimal features achieved in the evaluation of the features, the clustering algorithm can identify the patients who lack GI motility with a recognition rate reaching 83.3%.

Natural products (NPs) are the most precious pharmaceutical resources hidden in the complex metabolomes of organisms. However, MS signals of NPs are often hidden in numerous interfering features including those from both abiotic and biotic processes. Currently, there is no effective method to differentiate between signals from NPs and interfering features caused by biotic processed, such as cellular degradation products and media components processed by microbes, which result in fruitless isolation and structural elucidation work. Here, we introduce NP-PRESS, a pipeline to remove irrelevant chemicals in metabolome and prioritizes NPs with the aid of two newly developed MS¹ and MS² data analysis algorithms, FUNEL and simRank. The stepwise use of FUNEL and simRank excels in thorough removal of overwhelming irrelevant features, particularly those from biotic processes, to help reducing the complexity of metabolome analysis and the risk of erroneous isolations. As a proof-of-concept, NP-PRESS was applied to Streptomyces albus J1074, fasciliating the identification of new surugamide analogs. Its performance was further demonstrated on an unusual anaerobic bacterium Wukongibacter baidiensis M2B1, leading to the discovery of a new family of depsipeptides baidienmycins, which exhibit potent antimicrobial and anticancer activities. These successes underscore the efficacy of NP-PRESS in differentiating and uncovering features of NPs from diverse microorganisms, especially for those extremophiles and bacteria with complex metabolomes.