Recent advancements in cancer biology, microbiology, and bioengineering have spurred the development of engineered live biotherapeutics for targeted cancer therapy. In particular, natural tumor-targeting and probiotic bacteria have been engineered for controlled and sustained delivery of anticancer agents into the tumor microenvironment (TME). Here, we review the latest advancements in the development of engineered bacteria for cancer therapy and additional engineering strategies to potentiate the delivery of therapeutic payloads. We also explore the use of combination therapies comprising both engineered bacteria and conventional anticancer therapies for addressing intratumor heterogeneity. Finally, we discuss prospects for the development and clinical translation of engineered bacteria for cancer prevention and treatment. Using Bacteria as Cancer Treatment AgentsIn the landmark review paper 'The hallmarks of cancer' (see Glossary) and its sequel, Hanahan and Weinberg proposed that all cancers display common hallmarks or features that enable the transformation, growth, and progression of normal tissues into tumors [1,2]. Since then, these hallmarks have served as focus areas for researchers to develop novel therapeutic strategies. Current clinical therapies used to manage and treat most cancers include chemotherapy, immunotherapy, hormonal therapy, radiotherapy, and surgery. The choice of therapy, whether monotherapy or combination therapy, used in a treatment plan, varies widely depending on numerous factors, such as the cancer stage, grade, origin, and location within the human body. However, many current anticancer therapies have certain disadvantages, such as: (i) causing pharmacological adverse effects in normal cells; (ii) lacking the ability to penetrate solid tumor tissues; and (iii) being unable to eradicate all cancer cells in the tumor due to the inadvertent acquisition of drug resistance. Hence, there is a dire need to develop novel therapies that could supplement or serve as substitutes to conventional therapies used to treat cancer. In this regard, the use of bacteria for cancer therapy is a unique therapeutic option for consideration. Although there are 200 years of documented history of patients experiencing tumor regression after experimental bacterial infections, bacterial-based cancer treatments did not see much progress, due to reproducibility issues among patients and the rise of radiation therapy and chemotherapy [3]. To date, attenuated Mycobacterium bovis (Bacillus Calmette-Guerin, BCG) is the only bacterial-based cancer therapy that has been clinically approved by the US Food and Drug Administration (FDA) and has been used as one standard of care for high-risk patients with non-muscle-invasive bladder cancer (NMIBC) [4]. Yet, 30-50% of patients fail BCG treatment and~5% of patients have adverse effects, such as tissue sepsis [5].Recent advances in microbiology and bioengineering have restored interest in the development of bacteria-based cancer therapeutics due to their potential to addr...

The human microbiota is a complex community of commensal, symbiotic, and pathogenic microbes that play a crucial role in maintaining the homeostasis of human health. Such a homeostasis is maintained through the collective functioning of enzymatic genes responsible for the production of metabolites, enabling the interaction and signaling within microbiota as well as between microbes and the human host. Understanding microbial genes, their associated chemistries and functions would be valuable for engineering systemic metabolic pathways within the microbiota to manage human health and diseases. Given that there are many unknown gene metabolic functions and interactions, increasing efforts have been made to gain insights into the underlying functions of microbiota metabolism. This can be achieved through culture‐independent metagenomic approaches and metabolic modeling to simulate the microenvironment of human microbiota. In this article, the recent advances in metagenome mining and functional profiling for the discovery of the genetic and biochemical links in human microbiota metabolism as well as metabolic modeling for simulation and prediction of metabolic fluxes in the human microbiota are reviewed. This review provides useful insights into the understanding, reconstruction, and modulation of the human microbiota guided by the knowledge acquired from the basic understanding of the human microbiota metabolism.

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Scent plays an important role in influencing the brain and has been commonly used in psychological research. Much of such research has been conducted without the use of electroencephalography (EEG) to measure the response of the human brain to scent stimulus. Recent studies have involved the use of EEG to perform comparative studies on how different scents can affect brain activity. However, little has been done to analyze the trend of brain activity when a subject is repeatedly exposed to the same scent. This paper discusses the use of 4 features - Entropy Difference, Entropy Ratio, Entropy Time and Root Mean Square (RMS) to perform trend analysis of EEG signals in a repeated scent-exposure setting. The results show that different types of scents cause the brain to be stimulated at different degrees for each repeated exposure, giving rise to different trend patterns. It is also observed that the 4 features give similar trends for the same scent. This similarity allows us to combine the 4 features by forming a feature vector and plotting them in 3 dimensional (3D) space, using 3 repeated scent exposures as the axes. The region of space where the feature vector lies is represented by an ellipsoid, which can be used to characterize a particular scent. Unlike previous work, which did not characterize scent from EEG recordings, this paper investigates the different trends of scent after its repeated exposure to the human subject and by using the 3D representation to characterize the scent.

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We describe a novel array on array strategy intended to enhance the throughput of enzymatic activity screening using microarrays. This strategy consists of spotting a first array with large droplets of enzymes with varying concentrations and subsequently spotting a second array with small droplets of fluorogenic substrate on top of the enzyme array. By varying the array on array spotting patterns of different classes of enzyme (e.g., proteases, phosphatases, kinases) and their corresponding fluorogenic substrates, we have the unprecedented ability for testing enzymes and mixed samples in a multiplexed fashion within a single microarray slide. This new approach enables rapid enzyme characterization building upon a one enzyme on one slide droplet-based screening concept previously established.