Antibody-mediated delivery of chimeric protein degraders which target estrogen receptor alpha (ERα).

Chimeric molecules which effect intracellular degradation of target proteins via E3 ligase-mediated ubiquitination (e.g., PROTACs) are currently of high interest in medicinal chemistry. However, these entities are relatively large compounds that often possess molecular characteristics which may compromise oral bioavailability, solubility, and/or in vivo pharmacokinetic properties. Accordingly, we explored whether conjugation of chimeric degraders to monoclonal antibodies using technologies originally developed for cytotoxic payloads might provide alternate delivery options for these novel agents. In this report we describe the construction of several degrader-antibody conjugates comprised of two distinct ERα-targeting degrader entities and three independent ADC linker modalities. We subsequently demonstrate the antigen-dependent delivery to MCF7-neo/HER2 cells of the degrader payloads that are incorporated into these conjugates. We also provide evidence for efficient intracellular degrader release from one of the employed linkers. In addition, preliminary data are described which suggest that reasonably favorable in vivo stability properties are associated with the linkers utilized to construct the degrader conjugates.

Insights into taxonomy and phylogenetic relationships of eleven Aristolochia species based on chloroplast genome.

Introduction: The Aristolochia, as an important genus comprised of over 400 species, has attracted much interest because of its unique chemical and pharmacological properties. However, the intrageneric taxonomy and species identification within Aristolochia have long been difficult because of the complexity of their morphological variations and lack of high-resolution molecular markers. Methods: In this study, we sampled 11 species of Aristolochia collected from distinct habitats in China, and sequenced their complete chloroplast (cp) genomes. Results: The 11 cp genomes of Aristolochia ranged in size from 159,375bp (A. tagala) to 160,626 bp (A. tubiflora), each containing a large single-copy (LSC) region (88,914-90,251 bp), a small single-copy (SSC) region (19,311-19,917 bp), and a pair of inverted repeats (IR) (25,175-25,698 bp). These cp genomes contained 130-131 genes each, including 85 protein-coding genes (CDS), 8 ribosomal RNA genes, and 37-38 transfer RNA genes. In addition, the four types of repeats (forward, palindromic, reverse, and complement repeats) were examined in Aristolochia species. A. littoralis had the highest number of repeats (168), while A. tagala had the lowest number (42). The total number of simple sequence repeats (SSRs) is at least 99 in A. kwangsiensis, and, at most, 161 in A. gigantea. Interestingly, we detected eleven highly mutational hotspot regions, including six gene regions (clpP, matK, ndhF, psbT, rps16, trnK-UUU) and five intergenic spacer regions (ccsA-ndhD, psbZ-trnG-GCC, rpl33-rps18, rps16-trnQ-UUG, trnS-GCU-trnG-UCC). The phylogenetic analysis based on the 72 protein-coding genes showed that 11 Aristolochia species were divided into two clades which strongly supported the generic segregates of the subgenus Aristolochia and Siphisia. Discussion: This research will provide the basis for the classification, identification, and phylogeny of medicinal plants of Aristolochiaceae.

L-glutamine protects against enterohemorrhagic Escherichia coli infection by inhibiting bacterial virulence and enhancing host defense concurrently.

IMPORTANCE: The type 3 secretion system (T3SS) was obtained in many Gram-negative bacterial pathogens, and it is crucial for their pathogenesis. Environmental signals were found to be involved in the expression regulation of T3SS, which was vital for successful bacterial infection in the host. Here, we discovered that L-glutamine (Gln), the most abundant amino acid in the human body, could repress enterohemorrhagic Escherichia coli (EHEC) T3SS expression via nitrogen metabolism and therefore had potential as an antivirulence agent. Our in vitro and in vivo evidence demonstrated that Gln could decline EHEC infection by attenuating bacterial virulence and enhancing host defense simultaneously. We repurpose Gln as a potential treatment for EHEC infection accordingly.

Deep Learning for Reintegrating Biology.

The goal of this vision paper is to investigate the possible role that advanced machine learning techniques, especially deep learning (DL), could play in the reintegration of various biological disciplines. To achieve this goal, a series of operational, but admittedly very simplistic, conceptualizations have been introduced: Life has been taken as a multidimensional phenomenon that inhabits three physical dimensions (time, space, and scale) and biological research as establishing connection between different points in the domain of life. Each of these points hence denotes a position in time, space, and scale at which a life phenomenon of interest takes place. Using these conceptualizations, fragmentation of biology can be seen as the result of too few and especially too short-ranged connections. Reintegrating biology could then be accomplished by establishing more, longer ranged connections. DL methods appear to be very well suited for addressing this particular need at this particular time. Notwithstanding the numerous unsubstantiated claims regarding the capabilities of AI, DL networks represent a major advance in the ability to find complex relationships inside large data sets that would have not been accessible with traditional data analytic methods or to a human observer. In addition, ongoing advances in the automation of taking measurements from phenomena on all levels of biological organization continue to increase the number of large quantitative data sets that are available. These increasingly common data sets could serve as anchor points for making long-range connections by virtue of DL. However, connections within the domain of life are likely to be structured in a highly nonuniform fashion and hence it is necessary to develop methods, for example, theoretical, computational, and experimental, to determine linkage of biological data sets most likely to provide useful insights on a biological problem using DL. Finally, specific DL approaches and architectures should be developed to match the needs of reintegrating biology.

The Axes of Life: A Roadmap for Understanding Dynamic Multiscale Systems.

The biological challenges facing humanity are complex, multi-factorial, and are intimately tied to the future of our health, welfare, and stewardship of the Earth. Tackling problems in diverse areas, such as agriculture, ecology, and health care require linking vast datasets that encompass numerous components and spatio-temporal scales. Here, we provide a new framework and a road map for using experiments and computation to understand dynamic biological systems that span multiple scales. We discuss theories that can help understand complex biological systems and highlight the limitations of existing methodologies and recommend data generation practices. The advent of new technologies such as big data analytics and artificial intelligence can help bridge different scales and data types. We recommend ways to make such models transparent, compatible with existing theories of biological function, and to make biological data sets readable by advanced machine learning algorithms. Overall, the barriers for tackling pressing biological challenges are not only technological, but also sociological. Hence, we also provide recommendations for promoting interdisciplinary interactions between scientists.

Biodiversifying bioinspiration.

Bioinspiration-using insights into the function of biological systems for the development of new engineering concepts-is already a successful and rapidly growing field. However, only a small portion of the world's biodiversity has thus far been considered as a potential source for engineering inspiration. This means that vast numbers of biological systems of potentially high value to engineering have likely gone unnoticed. Even more important, insights into form and function that reside in the evolutionary relationships across the tree of life have not yet received attention by engineers. These insights could soon become accessible through recent developments in disparate areas of research; in particular, advancements in digitization of museum specimens, methods to describe and analyze complex biological shapes, quantitative prediction of biological function from form, and analysis of large digital data sets. Taken together, these emerging capabilities should make it possible to mine the world's known biodiversity as a natural resource for knowledge relevant to engineering. This transformation of bioinspiration would be very timely in the development of engineering, because it could yield exactly the kind of insights that are needed to make technology more autonomous, adaptive, and capable of operation in complex environments.