Autonomous and Assisted Control for Synthetic Microbiology

The control of microbes and microbial consortia to achieve specific functions requires synthetic circuits that can reliably cope with internal and external perturbations. Circuits that naturally evolved to regulate biological functions are frequently robust to alterations in their parameters. As the complexity of synthetic circuits increases, synthetic biologists need to implement such robust control “by design”. This is especially true for intercellular signaling circuits for synthetic consortia, where robustness is highly desirable, but its mechanisms remain unclear. Cybergenetics, the interface between synthetic biology and control theory, offers two approaches to this challenge: external (computer-aided) and internal (autonomous) control. Here, we review natural and synthetic microbial systems with robustness, and outline experimental approaches to implement such robust control in microbial consortia through population-level cybergenetics. We propose that harnessing natural intercellular circuit topologies with robust evolved functions can help to achieve similar robust control in synthetic intercellular circuits. A “hybrid biology” approach, where robust synthetic microbes interact with natural consortia and—additionally—with external computers, could become a useful tool for health and environmental applications.     

Ratiometric Zinc Biosensor Based on Bioluminescence Resonance Energy Transfer: Trace Metal Ion Determination with Tunable Response

Determination of metal ions such as zinc in solution remains an important task in analytical and biological chemistry. We describe a novel zinc ion biosensing approach using a carbonic anhydrase–Oplophorus luciferase fusion protein that employs bioluminescence resonance energy transfer (BRET) to transduce the level of free zinc as a ratio of emission intensities in the blue and orange portions of the spectrum. In addition to high sensitivity (below nanomolar levels) and selectivity, this approach allows both quantitative determination of “free” zinc ion (also termed “mobile” or “labile”) using bioluminescence ratios and determination of the presence of the ion above a threshold simply by the change in color of bioluminescence, without an instrument. The carbonic anhydrase metal ion sensing platform offers well-established flexibility in sensitivity, selectivity, and response kinetics. Finally, bioluminescence labeling has proven an effective approach for molecular imaging in vivo since no exciting light is required; the expressible nature of this sensor offers the prospect of imaging zinc fluxes in vivo.     

A Short ERAP2 That Binds IRAP Is Expressed in Macrophages Independently of Gene Variation

The M1 zinc metalloproteases ERAP1, ERAP2, and IRAP play a role in HLA-I antigen presentation by refining the peptidome either in the ER (ERAP1 and ERAP2) or in the endosomes (IRAP). They have also been entrusted with other, although less defined, functions such as the regulation of the angiotensin system and blood pressure. In humans, ERAP1 and IRAP are commonly expressed. ERAP2 instead has evolved under balancing selection that maintains two haplotypes, one of which undergoing RNA splicing leading to nonsense-mediated decay and loss of protein. Hence, likewise in rodents, wherein the ERAP2 gene is missing, about a quarter of the human population does not express ERAP2. We report here that macrophages, but not monocytes or other mononuclear blood cells, express and secrete an ERAP2 shorter form independent of the haplotype. The generation of this “short” ERAP2 is due to an autocatalytic cleavage within a distinctive structural motif and requires an acidic micro-environment. Remarkably, ERAP2 “short” binds IRAP and the two molecules are co-expressed in the endosomes as well as in the cell membrane. Of note, the same phenomenon could be observed in some cancer cells. These data prompt us to reconsider the role of ERAP2, which might have been maintained in humans due to fulfilling a relevant function in its “short” form.     

Nanotechnology Meets Oncology: A Perspective on the Role of the Personalized Nanoparticle-Protein Corona in the Development of Technologies for Pancreatic Cancer Detection

In recent years nanotechnology has opened exciting opportunities in the struggle against cancer. In 2007 Dawson and coworkers demonstrated that nanomaterials exposed to biological fluids are coated with plasma proteins that form the so-called “protein corona”. A few years later our joint research team made of physicists, chemists, biotechnologists, surgeons, oncologists, and bioinformaticians introduced the concept of “personalized protein corona” and demonstrated that it is unique for each human condition. This concept paved the way for the development of nano-enabled blood (NEB) tests for the diagnosis of pancreatic ductal adenocarcinoma (PDAC). These studies gave an impetus to serious work in the field that came to maturity in the late 2010s. In this special issue, we provide the reader with a comprehensive overview of the most significant discoveries of our research team in the field of PDAC detection. We focus on the main achievements with an emphasis on the fundamental aspects of this arena and how they shaped the integration of different scientific backgrounds towards the development of advanced diagnostic technologies. We conclude the review by outlining future perspectives and opportunities to transform the NEB tests into a reliable clinical diagnostic technology for early diagnosis, follow-up, and management of PDAC patients.     

Graphene Oxide (GO): A Promising Nanomaterial against Infectious Diseases Caused by Multidrug-Resistant Bacteria

Infectious diseases are major threat due to it being the main cause of enormous morbidity and mortality in the world. Multidrug-resistant (MDR) bacteria put an additional burden of infection leading to inferior treatment by the antibiotics of the latest generations. The emergence and spread of MDR bacteria (so-called “superbugs”), due to mutations in the bacteria and overuse of antibiotics, should be considered a serious concern. Recently, the rapid advancement of nanoscience and nanotechnology has produced several antimicrobial nanoparticles. It has been suggested that nanoparticles rely on very different mechanisms of antibacterial activity when compared to antibiotics. Graphene-based nanomaterials are fast emerging as “two-dimensional wonder materials” due to their unique structure and excellent mechanical, optical and electrical properties and have been exploited in electronics and other fields. Emerging trends show that their exceptional properties can be exploited for biomedical applications, especially in drug delivery and tissue engineering. Moreover, graphene derivatives were found to have in vitro antibacterial properties. In the recent years, there have been many studies demonstrating the antibacterial effects of GO on various types of bacteria. In this review article, we will be focusing on the aforementioned studies, focusing on the mechanisms, difference between the studies, limitations and future directions.     

From Molecular Recognition to the “Vehicles” of Evolutionary Complexity: An Informational Approach

Countless informational proposals and models have explored the singular characteristics of biological systems: from the initial choice of information terms in the early days of molecular biology to the current bioinformatic avalanche in this “omic” era. However, this was conducted, most often, within partial, specialized scopes or just metaphorically. In this paper, we attempt a consistent informational discourse, initially based on the molecular recognition paradigm, which addresses the main stages of biological organization in a new way. It considers the interconnection between signaling systems and information flows, between informational architectures and biomolecular codes, between controlled cell cycles and multicellular complexity. It also addresses, in a new way, a central issue: how new evolutionary paths are opened by the cumulated action of multiple variation engines or mutational ‘vehicles’ evolved for the genomic exploration of DNA sequence space. Rather than discussing the possible replacement, extension, or maintenance of traditional neo-Darwinian tenets, a genuine informational approach to evolutionary phenomena is advocated, in which systemic variation in the informational architectures may induce differential survival (self-construction, self-maintenance, and reproduction) of biological agents within their open ended environment.     

Quantitative Analysis of the Potency of Equimolar Two-Drug Combinations and Combi-Molecules Involving Kinase Inhibitors In Vitro: The Concept of Balanced Targeting

The median-effect principle proposed by Chou and Talalay is the most effective approach to parameterize interactions between several agents in combination. However, this method cannot be used to evaluate the effectiveness of equimolar drug combinations, which are comparative references for dual-targeting molecular design. Here, using data acquired through the development of “combi-molecules” blocking two kinases (e.g., EGFR-c-Src and EGFR-c-Met), we established potency indices for equimolar and dual-targeted inhibitors. If the fold difference (κ) between the IC50 of the two individual kinase inhibitors was >6, the IC50 of their equimolar combination resembled that of the more potent inhibitor. Hence, the “combi-targeting” of the two kinases was considered “imbalanced” and the combination ineffective. However, if κ ≤ 6, the IC50 of the combination fell below that of each individual drug and the combi-targeting was considered “balanced” and the combination effective. We also showed that combi-molecules should be compared with equimolar combinations only under balanced conditions and propose a new parameter Ω for validating their effectiveness. A multi-targeted drug is effective if Ω < 1, where Ω is defined as the IC50 of the drug divided by that of the corresponding equimolar combination. Our study provides a methodology to determine the in vitro potency of equimolar two-drug combinations as well as combi-/hybrid molecules inhibiting two different kinase targets.     

Multidimensional Phylogenetic Metrics Identify Class I Aminoacyl-tRNA Synthetase Evolutionary Mosaicity and Inter-Modular Coupling

The role of aminoacyl-tRNA synthetases (aaRS) in the emergence and evolution of genetic coding poses challenging questions concerning their provenance. We seek evidence about their ancestry from curated structure-based multiple sequence alignments of a structurally invariant “scaffold” shared by all 10 canonical Class I aaRS. Three uncorrelated phylogenetic metrics—mutation frequency, its uniformity, and row-by-row cladistic congruence—imply that the Class I scaffold is a mosaic assembled from successive genetic sources. Metrics for different modules vary in accordance with their presumed functionality. Sequences derived from the ATP– and amino acid– binding sites exhibit specific two-way coupling to those derived from Connecting Peptide 1, a third module whose metrics suggest later acquisition. The data help validate: (i) experimental fragmentations of the canonical Class I structure into three partitions that retain catalytic activities in proportion to their length; and (ii) evidence that the ancestral Class I aaRS gene also encoded a Class II ancestor in frame on the opposite strand. A 46-residue Class I “protozyme” roots the Class I tree prior to the adaptive radiation of the Rossmann dinucleotide binding fold that refined substrate discrimination. Such rooting implies near simultaneous emergence of genetic coding and the origin of the proteome, resolving a conundrum posed by previous inferences that Class I aaRS evolved after the genetic code had been implemented in an RNA world. Further, pinpointing discontinuous enhancements of aaRS fidelity establishes a timeline for the growth of coding from a binary amino acid alphabet.     

Analysis of Variability of Functionals of Recombinant Protein Production Trajectories Based on Limited Data

Making statistical inference on quantities defining various characteristics of a temporally measured biochemical process and analyzing its variability across different experimental conditions is a core challenge in various branches of science. This problem is particularly difficult when the amount of data that can be collected is limited in terms of both the number of replicates and the number of time points per process trajectory. We propose a method for analyzing the variability of smooth functionals of the growth or production trajectories associated with such processes across different experimental conditions. Our modeling approach is based on a spline representation of the mean trajectories. We also develop a bootstrap-based inference procedure for the parameters while accounting for possible multiple comparisons. This methodology is applied to study two types of quantities—the “time to harvest” and “maximal productivity”—in the context of an experiment on the production of recombinant proteins. We complement the findings with extensive numerical experiments comparing the effectiveness of different types of bootstrap procedures for various tests of hypotheses. These numerical experiments convincingly demonstrate that the proposed method yields reliable inference on complex characteristics of the processes even in a data-limited environment where more traditional methods for statistical inference are typically not reliable.     

BonMOLière: Small-Sized Libraries of Readily Purchasable Compounds,Optimized to Produce Genuine Hits in Biological Screens across the Protein Space

Experimental screening of large sets of compounds against macromolecular targets is a key strategy to identify novel bioactivities. However, large-scale screening requires substantial experimental resources and is time-consuming and challenging. Therefore, small to medium-sized compound libraries with a high chance of producing genuine hits on an arbitrary protein of interest would be of great value to fields related to early drug discovery, in particular biochemical and cell research. Here, we present a computational approach that incorporates drug-likeness, predicted bioactivities, biological space coverage, and target novelty, to generate optimized compound libraries with maximized chances of producing genuine hits for a wide range of proteins. The computational approach evaluates drug-likeness with a set of established rules, predicts bioactivities with a validated, similarity-based approach, and optimizes the composition of small sets of compounds towards maximum target coverage and novelty. We found that, in comparison to the random selection of compounds for a library, our approach generates substantially improved compound sets. Quantified as the “fitness” of compound libraries, the calculated improvements ranged from +60% (for a library of 15,000 compounds) to +184% (for a library of 1000 compounds). The best of the optimized compound libraries prepared in this work are available for download as a dataset bundle (“BonMOLière”).     

Analysis of Dormancy-Associated Transcriptional Networks Reveals a Shared Quiescence Signature in Lung and Colorectal Cancer

Quiescent cancer cells (QCCs) are a common feature of solid tumors, representing a major obstacle to the long-term success of cancer therapies. We isolated QCCs ex vivo from non-small cell lung cancer (NSCLC) and colorectal cancer (CRC) xenografts with a label-retaining strategy and compared QCCs gene expression profiles to identify a shared “quiescence signature”. Principal Component Analysis (PCA) revealed a specific component neatly discriminating quiescent and replicative phenotypes in NSCLC and CRC. The discriminating component showed significant overlapping, with 688 genes in common including ZEB2, a master regulator of stem cell plasticity and epithelial-to-mesenchymal transition (EMT). Gene set enrichment analysis showed that QCCs of both NSCLC and CRC had an increased expression of factors related to stemness/self renewal, EMT, TGF-β, morphogenesis, cell adhesion and chemotaxis, whereas proliferating cells overexpressed Myc targets and factors involved in RNA metabolism. Eventually, we analyzed in depth by means of a complex network approach, both the ‘morphogenesis module’ and the subset of differentially expressed genes shared by NCSLC and CRC. This allowed us to recognize different gene regulation network wiring for quiescent and proliferating cells and to underpin few genes central for network integration that may represent new therapeutic vulnerabilities. Altogether, our results highlight common regulatory pathways in QCCs of lung and colorectal tumors that may be the target of future therapeutic interventions.     

Gut Microbiota as a Source of Uremic Toxins

Uremic retention solutes are the compounds that accumulate in the blood when kidney excretory function is impaired. Some of these compounds are toxic at high concentrations and are usually known as “uremic toxins”. The cumulative detrimental effect of uremic toxins results in numerous health problems and eventually mortality during acute or chronic uremia, especially in end-stage renal disease. More than 100 different solutes increase during uremia; however, the exact origin for most of them is still debatable. There are three main sources for such compounds: exogenous ones are consumed with food, whereas endogenous ones are produced by the host metabolism or by symbiotic microbiota metabolism. In this article, we identify uremic retention solutes presumably of gut microbiota origin. We used database analysis to obtain data on the enzymatic reactions in bacteria and human organisms that potentially yield uremic retention solutes and hence to determine what toxins could be synthesized in bacteria residing in the human gut. We selected biochemical pathways resulting in uremic retention solutes synthesis related to specific bacterial strains and revealed links between toxin concentration in uremia and the proportion of different bacteria species which can synthesize the toxin. The detected bacterial species essential for the synthesis of uremic retention solutes were then verified using the Human Microbiome Project database. Moreover, we defined the relative abundance of human toxin-generating enzymes as well as the possibility of the synthesis of a particular toxin by the human metabolism. Our study presents a novel bioinformatics approach for the elucidation of the origin of both uremic retention solutes and uremic toxins and for searching for the most likely human microbiome producers of toxins that can be targeted and used for the therapy of adverse consequences of uremia.     

“Dividing and Conquering” and “Caching” in Molecular Modeling

Molecular modeling is widely utilized in subjects including but not limited to physics, chemistry, biology, materials science and engineering. Impressive progress has been made in development of theories, algorithms and software packages. To divide and conquer, and to cache intermediate results have been long standing principles in development of algorithms. Not surprisingly, most important methodological advancements in more than half century of molecular modeling are various implementations of these two fundamental principles. In the mainstream classical computational molecular science, tremendous efforts have been invested on two lines of algorithm development. The first is coarse graining, which is to represent multiple basic particles in higher resolution modeling as a single larger and softer particle in lower resolution counterpart, with resulting force fields of partial transferability at the expense of some information loss. The second is enhanced sampling, which realizes “dividing and conquering” and/or “caching” in configurational space with focus either on reaction coordinates and collective variables as in metadynamics and related algorithms, or on the transition matrix and state discretization as in Markov state models. For this line of algorithms, spatial resolution is maintained but results are not transferable. Deep learning has been utilized to realize more efficient and accurate ways of “dividing and conquering” and “caching” along these two lines of algorithmic research. We proposed and demonstrated the local free energy landscape approach, a new framework for classical computational molecular science. This framework is based on a third class of algorithm that facilitates molecular modeling through partially transferable in resolution “caching” of distributions for local clusters of molecular degrees of freedom. Differences, connections and potential interactions among these three algorithmic directions are discussed, with the hope to stimulate development of more elegant, efficient and reliable formulations and algorithms for “dividing and conquering” and “caching” in complex molecular systems.     

DAMPs and RAGE Pathophysiology at the Acute Phase of Brain Injury: An Overview

Early or primary injury due to brain aggression, such as mechanical trauma, hemorrhage or is-chemia, triggers the release of damage-associated molecular patterns (DAMPs) in the extracellular space. Some DAMPs, such as S100B, participate in the regulation of cell growth and survival but may also trigger cellular damage as their concentration increases in the extracellular space. When DAMPs bind to pattern-recognition receptors, such as the receptor of advanced glycation end-products (RAGE), they lead to non-infectious inflammation that will contribute to necrotic cell clearance but may also worsen brain injury. In this narrative review, we describe the role and ki-netics of DAMPs and RAGE at the acute phase of brain injury. We searched the MEDLINE database for “DAMPs” or “RAGE” or “S100B” and “traumatic brain injury” or “subarachnoid hemorrhage” or “stroke”. We selected original articles reporting data on acute brain injury pathophysiology, from which we describe DAMPs release and clearance upon acute brain injury, and the implication of RAGE in the development of brain injury. We will also discuss the clinical strategies that emerge from this overview in terms of biomarkers and therapeutic perspectives     

Methods of Attenuating Ischemia-Reperfusion Injury in Liver Transplantation for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most frequent indications for liver transplantation. However, the transplantation is ultimately associated with the occurrence of ischemia-reperfusion injury (IRI). It affects not only the function of the graft but also significantly worsens the oncological results. Various methods have been used so far to manage IRI. These include the non-invasive approach (pharmacotherapy) and more advanced options encompassing various types of liver conditioning and machine perfusion. Strategies aimed at shortening ischemic times and better organ allocation pathways are still under development as well. This article presents the mechanisms responsible for IRI, its impact on treatment outcomes, and strategies to mitigate it. An extensive review of the relevant literature using MEDLINE (PubMed) and Scopus databases until September 2020 was conducted. Only full-text articles written in English were included. The following search terms were used: “ischemia reperfusion injury”, “liver transplantation”, “hepatocellular carcinoma”, “preconditioning”, “machine perfusion”.     

High Resolution Structure of the Mature Capsid of Ralstonia solanacearum Bacteriophage ϕRSA1 by Cryo-Electron Microscopy

The ϕRSA1 bacteriophage has been isolated from Ralstonia solanacearum, a gram negative bacteria having a significant economic impact on many important crops. We solved the three-dimensional structure of the ϕRSA1 mature capsid to 3.9 Å resolution by cryo-electron microscopy. The capsid shell, that contains the 39 kbp of dsDNA genome, has an icosahedral symmetry characterized by an unusual triangulation number of T = 7, dextro. The ϕRSA1 capsid is composed solely of the polymerization of the major capsid protein, gp8, which exhibits the typical “Johnson” fold first characterized in E. coli bacteriophage HK97. As opposed to the latter, the ϕRSA1 mature capsid is not stabilized by covalent crosslinking between its subunits, nor by the addition of a decoration protein. We further describe the molecular interactions occurring between the subunits of the ϕRSA1 capsid and their relationships with the other known bacteriophages.     

Anti-Biofilm Performance of Three Natural Products against Initial Bacterial Attachment

Marine bacteria contribute significantly towards the fouling consortium, both directly (modern foul release coatings fail to prevent “slime” attachment) and indirectly (biofilms often excrete chemical cues that attract macrofouling settlement). This study assessed the natural product anti-biofilm performance of an extract of the seaweed, Chondrus crispus, and two isolated compounds from terrestrial sources, (+)-usnic acid and juglone, against two marine biofilm forming bacteria, Cobetia marina and Marinobacter hydrocarbonoclasticus. Bioassays were developed using quantitative imaging and fluorescent labelling to test the natural products over a range of concentrations against initial bacterial attachment. All natural products affected bacterial attachment; however, juglone demonstrated the best anti-biofilm performance against both bacterial species at a concentration range between 5–20 ppm. In addition, for the first time, a dose-dependent inhibition (hormetic) response was observed for natural products against marine biofilm forming bacteria.     

Embryonic-Derived Myb− Macrophages Enhance Bacterial Clearance and Improve Survival in Rat Sepsis

Peritoneal resident macrophages play a key role in combating sepsis in the peritoneal cavity. We sought to determine if peritoneal transplantation of embryonic Myb⁻ “peritoneal-like” macrophages attenuate abdominal fecal sepsis. Directed differentiation of rodent pluripotent stem cells (PSCs) was used in factor-defined media to produce embryonic-derived large “peritoneal-like” macrophages (Ed-LPM) that expressed peritoneal macrophage markers and demonstrated phagocytic capacity. Preclinical in vivo studies determined Ed-LPM efficacy in rodent abdominal fecal sepsis with or without Meropenem. Ex vivo studies explored the mechanism and effects of Ed-LPM on host immune cell number and function, including phagocytosis, reactive oxygen species (ROS) production, efferocytosis and apoptosis. Ed-LPM reduced sepsis severity by decreasing bacterial load in the liver, spleen and lungs. Ed-LPM therapy significantly improved animal survival by ~30% and reduced systemic bacterial burden to levels comparable to Meropenem therapy. Ed-LPM therapy decreased peritoneal TNFα while increasing IL-10 concentrations. Ed-LPMs enhanced peritoneal macrophage phagocytosis of bacteria, increased macrophage production of ROS and restored homeostasis via apoptosis and efferocytosis-induced clearance of neutrophils. In conclusion, Ed-LPM reduced systemic sepsis severity, improved survival and reduced bacterial load by enhancing peritoneal macrophage bacterial phagocytosis and killing and clearance of intra-peritoneal neutrophils. Macrophage therapy may be a potential strategy to address sepsis.