Using antibody synergy to engineer a high potency biologic cocktail against C. difficile

We applied a mathematical framework originally used to model the effects of multiple inhibitors on enzyme activity to guide the development a therapeutic antibody cocktail, LMN-201, to prevent and treat C. difficile infection (CDI). CDI causes hundreds of thousands of cases of severe, often recurrent diarrhea and colitis in the United States annually and is associated with significant morbidity and mortality worldwide. Current therapies for preventing recurrent CDI are only partially successful, and there are no options available to prevent initial bouts of CDI in at-risk populations. Almost all antibody therapies have been developed and administered as monotherapies. Antibody cocktails are relatively rare even though they have the potential to greatly increase efficacy. One reason for this is our limited understanding of how antibody interactions can enhance potency, which makes it difficult to identify and develop antibodies that can be assembled into optimally effective cocktails. In contrast to the view that antibody synergies depend on unusual instances of cooperativity or allostery, we show that synergistic efficacy requires nothing more than that the antibodies bind independently to distinct epitopes on a common target. Therefore, synergy may be achieved much more readily than is generally appreciated. Due to synergy the LMN-201 antibody cocktail, which targets the C. difficile exotoxin B (TcdB), is 300- to 3000-fold more potent at neutralizing the most clinically prevalent TcdB toxin types than bezlotoxumab, the only monoclonal antibody currently approved for treatment or prevention of CDI. The efficacy of LMN-201 is further enhanced by inclusion of a phage-derived endolysin that destroys the C. difficile bacterium, and which therefore has a complementary mechanism of action to the antibody cocktail. These observations may serve as a paradigm for the development of high potency biologic cocktails against targets that have proven challenging for single-agent therapies.

Distributed Mechanism for Detecting Average Consensus with Maximum-Degree Weights in Bipartite Regular Graphs

In recent decades, distributed consensus-based algorithms for data aggregation have been gaining in importance in wireless sensor networks since their implementation as a complementary mechanism can ensure sensor-measured values with high reliability and optimized energy consumption in spite of imprecise sensor readings. In the presented article, we address the average consensus algorithm over bipartite regular graphs, where the application of the maximum-degree weights causes the divergence of the algorithm. We provide a spectral analysis of the algorithm, propose a distributed mechanism to detect whether a graph is bipartite regular, and identify how to reconfigure the algorithm so that the convergence of the average consensus algorithm is guaranteed over bipartite regular graphs. More specifically, we identify in the article that only the largest and the smallest eigenvalues of the weight matrix are located on the unit circle; the sum of all the inner states is preserved at each iteration despite the algorithm divergence; and the inner states oscillate between two values close to the arithmetic means determined by the initial inner states from each disjoint subset. The proposed mechanism utilizes the first-order forward and backward finite-difference of the inner states (more specifically, five conditions are proposed) to detect whether a graph is bipartite regular or not. Subsequently, the mixing parameter of the algorithm can be reconfigured the way it is identified in this study whereby the convergence of the algorithm is ensured in bipartite regular graphs. In the experimental part, we tested our mechanism over randomly generated bipartite regular graphs, random graphs, and random geometric graphs with various parameters, thereby identifying its very high detection rate and proving that the algorithm can estimate the arithmetic mean with high precision (like in error-free scenarios) after the suggested reconfiguration.

The neural representation of absolute direction during mental navigation in conceptual spaces

When humans mentally “navigate” bidimensional uniform conceptual spaces, they recruit the same grid-like and distance codes typically evoked when exploring the physical environment. Here, using fMRI, we show evidence that conceptual navigation also elicits another kind of spatial code: that of absolute direction. This code is mostly localized in the medial parietal cortex, where its strength predicts participants’ comparative semantic judgments. It may provide a complementary mechanism for conceptual navigation outside the hippocampal formation.

Effects of go/no-go training on food-related action tendencies, liking and choice

Inhibitory control training effects on behaviour (e.g. ‘healthier’ food choices) can be driven by changes in affective evaluations of trained stimuli, and theoretical models indicate that changes in action tendencies may be a complementary mechanism. In this preregistered study, we investigated the effects of food-specific go/no-go training on action tendencies, liking and impulsive choices in healthy participants. In the training task, energy-dense foods were assigned to one of three conditions: 100% inhibition (no-go), 0% inhibition (go) or 50% inhibition (control). Automatic action tendencies and liking were measured pre- and post-training for each condition. We found that training did not lead to changes in approach bias towards trained foods (go and no-go relative to control), but we warrant caution in interpreting this finding as there are important limitations to consider for the employed approach–avoidance task. There was only anecdotal evidence for an effect on food liking, but there was evidence for contingency learning during training, and participants were on average less likely to choose a no-go food compared to a control food after training. We discuss these findings from both a methodological and theoretical standpoint and propose that the mechanisms of action behind training effects be investigated further.

A Complementary Mechanism of Bacterial mRNA Translation Inhibition by Tetracyclines

Tetracycline has positively impacted human health as well as the farming and animal industries. Its extensive usage and versatility led to the spread of resistance mechanisms followed by the development of new variants of the antibiotic. Tetracyclines inhibit bacterial growth by impeding the binding of elongator tRNAs to the ribosome. However, a small number of reports indicated that Tetracyclines could also inhibit translation initiation, yet the molecular mechanism remained unknown. Here, we use biochemical and computational methods to study how Oxytetracycline (Otc), Demeclocycline (Dem), and Tigecycline (Tig) affect the translation initiation phase of protein synthesis. Our results show that all three Tetracyclines induce Initiation Factor IF3 to adopt a compact conformation on the 30S ribosomal subunit, similar to that induced by Initiation Factor IF1. This compaction was faster for Tig than Dem or Otc. Furthermore, all three tested tetracyclines affected IF1-bound 30S complexes. The dissociation rate constant of IF1 in early 30S complexes was 14-fold slower for Tig than Dem or Otc. Late 30S initiation complexes (30S pre-IC or IC) exhibited greater IF1 stabilization by Tig than for Dem and Otc. Tig and Otc delayed 50S joining to 30S initiation complexes (30S ICs). Remarkably, the presence of Tig considerably slowed the progression to translation elongation and retained IF1 in the resulting 70S initiation complex (70S IC). Molecular modeling of Tetracyclines bound to the 30S pre-IC and 30S IC indicated that the antibiotics binding site topography fluctuates along the initiation pathway. Mainly, 30S complexes show potential contacts between Dem or Tig with IF1, providing a structural rationale for the enhanced affinity of the antibiotics in the presence of the factor. Altogether, our data indicate that Tetracyclines inhibit translation initiation by allosterically perturbing the IF3 layout on the 30S, retaining IF1 during 70S IC formation, and slowing the transition toward translation elongation. Thus, this study describes a new complementary mechanism by which Tetracyclines may inhibit bacterial protein synthesis.

Tissue Specific Heterophile Aggregation of Protogenes Promoted Heterosis

A plethora of studies have described heterosis or hybrid vigor; however, a global understanding of its regulation and the transmission of transcriptional levels between parents and hybrid has yet to be attained. To improve our understanding the molecular mechanisms controlling maize heterosis, we used an incomplete diallel cross design consisting of four elite maize inbred lines and six of their hybrids to measure the degree of variation in gene expression between the parents and their hybrids. We found that differentially expressed genes (DEGs) drove diversity of tissue specific heterosis and that heterophile expression was a generally complementary mechanism of gene expression in hybrids. However, the full expression of heterosis was due to the proportion of super dominant gene expression patterns that aggregate the regulatory network of dominant genes in response to adversity, and thus promotes heterosis in hybrids. Our results provide a new understanding and perspective into the regulatory mechanisms that control heterosis and represent an important step towards a more comprehensive explanation of heterosis in maize.

ARF2 represses expression of plant GRF transcription factors in a complementary mechanism to microRNA miR396

Members of the GROWTH REGULATING FACTOR (GRF) family of transcription factors play key roles in the promotion of plant growth and development. Many GRFs are post-transcriptionally repressed by microRNA (miRNA) miR396, an evolutionarily conserved small RNA, which restricts their expression to proliferative tissue. We performed a comprehensive analysis of the GRF family in eudicot plants and found that in many species all the GRFs have a miR396-binding site. Yet, we also identified GRFs with mutations in the sequence recognized by miR396, suggesting a partial or complete release of their post-transcriptional repression. Interestingly, Brassicaceae species share a group of GRFs that lack miR396 regulation, including Arabidopsis GRF5 and GRF6. We show that instead of miR396-mediated post-transcriptional regulation, the spatiotemporal control of GRF5 is achieved through evolutionarily conserved promoter sequences, and that AUXIN RESPONSE FACTOR 2 (ARF2) binds to such conserved sequences to repress GRF5 expression. Furthermore, we demonstrate that the unchecked expression of GRF5 in arf2 mutants is responsible for the increased cell number of arf2 leaves. The results describe a switch in the repression mechanisms that control the expression of GRFs and mechanistically link the control of leaf growth by miR396, GRFs, and ARF2 transcription factors.

The Osteoporosis/Microbiota Linkage: The Role of miRNA

Hundreds of trillions of bacteria are present in the human body in a mutually beneficial symbiotic relationship with the host. A stable dynamic equilibrium exists in healthy individuals between the microbiota, host organism, and environment. Imbalances of the intestinal microbiota contribute to the determinism of various diseases. Recent research suggests that the microbiota is also involved in the regulation of the bone metabolism, and its alteration may induce osteoporosis. Due to modern molecular biotechnology, various mechanisms regulating the relationship between bone and microbiota are emerging. Understanding the role of microbiota imbalances in the development of osteoporosis is essential for the development of potential osteoporosis prevention and treatment strategies through microbiota targeting. A relevant complementary mechanism could be also constituted by the permanent relationships occurring between microbiota and microRNAs (miRNAs). miRNAs are a set of small non-coding RNAs able to regulate gene expression. In this review, we recapitulate the physiological and pathological meanings of the microbiota on osteoporosis onset by governing miRNA production. An improved comprehension of the relations between microbiota and miRNAs could furnish novel markers for the identification and monitoring of osteoporosis, and this appears to be an encouraging method for antagomir-guided tactics as therapeutic agents.

Autoimmune Hepatitis and Stellate Cells: An Insight into the Role of Autophagy

Autoimmune hepatitis is a necroinflammatory process of liver, featuring interface hepatitis by T cells, macrophages and plasma cells that invade to periportal parenchyma. In this process, a variety of cytokines are secreted and liver tissues undergo fibrogenesis, resulting in the apoptosis of hepatocytes. Autophagy is a complementary mechanism for restraining intracellular pathogens to which the innate immune system does not provide efficient endocytosis. Hepatocytes with their particular regenerative features are normally in a quiescent state, and, autophagy controls the accumulation of excess products, therefore the liver serves as a basic model for the study of autophagy. Impairment of autophagy in the liver causes the accumulation of damaged organelles, misfolded proteins and exceeded lipids in hepatocytes as seen in metabolic diseases. In this review, we introduce autoimmune hepatitis in association with autophagy signaling. We also discuss some genes and proteins of autophagy, their regulatory roles in the activation of hepatic stellate cells and the importance of lipophagy and tyrosine kinase in hepatic fibrogenesis. In order to provide a comprehensive overview of the regulatory role of autophagy in autoimmune hepatitis, the pathway analysis of autophagy in autoimmune hepatitis is also included in this article.