Mantovani, S. | Search bioRxiv

bioRxiv2023

VDAC1 selective molecules promote patients-derived cancer organoids death through mitochondrial-dependent metabolic interference

In the continuous pursuit of advanced cancer therapeutics, our research unveils the potential to selectively target Voltage-Dependent Anion-selective Channel isoform 1 (VDAC1), a pivotal component in cellular metabolism and apoptosis. VDAC1s role in metabolic rewiring and its subsequent prominence in many cancer types offer a unique intervention point. The incorporation of a systematic, in silico to in vitro methodology identified novel VA (VDAC-Antagonist) molecules with the capability to selectively bind to VDAC1, displaying a substantial specificity towards cancer cells while sparing healthy ones. This research first led to the revelation of a specialized VDAC1 pocket, which accommodates the binding of these VA molecules, thereby instigating a selective displacement of NADH. The coenzyme is a critical metabolic substrate, and its displacement ensues in notable mitochondrial distress and a reduction in cell proliferation, specifically in cancer cells. Furthermore, meticulous analysis using organoids derived from intrahepatic cholangiocarcinoma patients (iCCA) demonstrated a dose-dependent reduction in cell viability upon treatment with VA molecules, correlating with the findings from commercial cell lines. Interestingly, VA molecules significantly reduced cell viability and demonstrated a lower impact on healthy cells than conventional treatments like gemcitabine. This differential impact is possibly due to the elevated expression of VDAC1 in various cancer cell lines, rendering them more susceptible to metabolic disruptions induced by VA molecules. This endeavor uncovers a multifaceted approach to cancer treatment, involving meticulous targeting of metabolic gatekeepers like VDAC1 using novel entities, thereby paving the way for developing more selective and refined cancer therapeutics. The identified VA molecules, albeit in the nascent stages, represent promising candidates for further optimization and development, potentially revolutionizing treatment modalities in cancer therapy through precise metabolic interventions.

biophysics↗

bioRxiv2023

An Automated Scientist to Design and Optimize Microbial Strains for the Industrial Production of Small Molecules

Engineering microbes to synthesize molecules of societal value has historically been a time consuming and artisanal process, with the synthesis of each new non-native molecule typically warranting its own separate publication. Because most microbial strain engineering efforts leverage a finite number of common metabolic engineering design tactics, we reasoned that automating these design steps would help create a pipeline that can quickly, cheaply, and reliably generate so-called microbial factories. In this work we describe the design and implementation of a computational system, an Automated Scientist we call Lila, which handles all metabolic engineering design and optimization through the design-build-test-learn (DBTL) paradigm. Lila generates metabolic routes, identifies relevant genetic elements for perturbation, and specifies the design and re-design of microbial strains in a matter of seconds to minutes. Strains specified by Lila are then built and subsequently phenotyped as part of a largely automated in-house pipeline. Humans remain in-the-loop to curate choices made by the system, helping for example to refine the metabolic model or suggest custom protein modifications. Lila attempted to build strains that could produce 454 biochemically diverse molecules with precursors located broadly throughout the metabolism of two microbial hosts, Saccharomyces cerevisiae and Escherichia coli. Notably, we observed the highest published titers for the molecule naringenin, the metabolic precursor to flavonoids. In total we created hundreds of thousands of microbial strains capable of overproducing 242 molecules, of which 180 are not native to S. cerevisiae or E. coli.

synthetic biology↗