To perform well in biotechnology applications, synthetic genetic oscillators must be engineered to allow independent modulation of amplitude and period. This need is currently unmet. Here, we demonstrate computationally how two classic genetic oscillators, the dual-feedback oscillator and the repressilator, can be re-designed to provide independent control of amplitude and period and improve tunability-that is, a broad dynamic range of periods and amplitudes accessible through the input "dials." Our approach decouples frequency and amplitude modulation by incorporating an orthogonal "sink module" where the key molecular species are channeled for enzymatic degradation. This sink module maintains fast oscillation cycles while alleviating the translational coupling between the oscillator's transcription factors and output. We characterize the behavior of our re-designed oscillators over a broad range of physiologically reasonable parameters, explain why this facilitates broader function and control, and provide general design principles for building synthetic genetic oscillators that are more precisely controllable.

Protected Designation of Origin (PDO) labeling of cheeses has been established by the European Union (EU) as a quality policy that assures the authenticity of a cheese produced in a specific region by applying traditional production methods. However, currently used scientific methods for differentiating and establishing PDO are limited in terms of time, cost, accuracy and their ability to identify through quantifiable methods PDO fraud. Cheese microbiome is a dynamic community that progressively changes throughout ripening, contributing via its metabolism to unique qualitative and sensorial characteristics that differentiate each cheese. High Throughput Sequencing (HTS) methodologies have enabled the more precise identification of the microbial communities developed in fermented cheeses, characterization of their population dynamics during the cheese ripening process, as well as their contribution to the development of specific organoleptic and physio-chemical characteristics. Therefore, their application may provide an additional tool to identify the key microbial species that contribute to PDO cheeses unique sensorial characteristics and to assist to define their typicityin order to distinguish them from various fraudulent products. Additionally, they may assist the cheese-makers to better evaluate the quality, as well as the safety of their products. In this structured literature review indications are provided on the potential for defining PDO enabling differentiating factors based on distinguishable microbial communities shaped throughout the ripening procedures associated to cheese sensorial characteristics, as revealed through metagenomic and metatranscriptomic studies. Conclusively, HTS applications, even though still underexploited, have the potential to demonstrate how the cheese microbiome can affect the ripening process and sensorial characteristics formation via the catabolism of the available nutrients and interplay with other compounds of the matrix and/or production of microbial origin metabolites and thus their further quality enhancement.

This study aims to highlight SARS-COV-2 mutations which are associated with increased or decreased viral virulence. We utilize genetic data from all strains available from GISAID and countries’ regional information, such as deaths and cases per million, as well as COVID-19-related public health austerity measure response times. Initial indications of selective advantage of specific mutations can be obtained from calculating their frequencies across viral strains. By applying modelling approaches, we provide additional information that is not evident from standard statistics or mutation frequencies alone. We therefore, propose a more precise way of selecting informative mutations. We highlight two interesting mutations found in genes N (P13L) and ORF3a (Q57H). The former appears to be significantly associated with decreased deaths and cases per million according to our models, while the latter shows an opposing association with decreased deaths and increased cases per million. Moreover, protein structure prediction tools show that the mutations infer conformational changes to the protein that significantly alter its structure when compared to the reference protein.

Huntington’s disease is a rare neurodegenerative disease caused by a cytosine–adenine–guanine (CAG) trinucleotide expansion in the Huntingtin (HTT) gene. Although Huntington’s disease (HD) is well studied, the pathophysiological mechanisms, genes and metabolites involved in HD remain poorly understood. Systems bioinformatics can reveal synergistic relationships among different omics levels and enables the integration of biological data. It allows for the overall understanding of biological mechanisms, pathways, genes and metabolites involved in HD. The purpose of this study was to identify the differentially expressed genes (DEGs), pathways and metabolites as well as observe how these biological terms differ between the pre-symptomatic and symptomatic HD stages. A publicly available dataset from the Gene Expression Omnibus (GEO) was analyzed to obtain the DEGs for each HD stage, and gene co-expression networks were obtained for each HD stage. Network rewiring, highlights the nodes that change most their connectivity with their neighbors and infers their possible implication in the transition between different states. The CACNA1I gene was the mostly highly rewired node among pre-symptomatic and symptomatic HD network. Furthermore, we identified AF198444 to be common between the rewired genes and DEGs of symptomatic HD. CNTN6, DEK, LTN1, MST4, ZFYVE16, CEP135, DCAKD, MAP4K3, NUPL1 and RBM15 between the DEGs of pre-symptomatic and DEGs of symptomatic HD and CACNA1I, DNAJB14, EPS8L3, HSDL2, SNRPD3, SOX12, ACLY, ATF2, BAG5, ERBB4, FOCAD, GRAMD1C, LIN7C, MIR22, MTHFR, NABP1, NRG2, OTC, PRAMEF12, SLC30A10, STAG2 and Y16709 between the rewired genes and DEGs of pre-symptomatic HD. The proteins encoded by these genes are involved in various biological pathways such as phosphatidylinositol-4,5-bisphosphate 3-kinase activity, cAMP response element-binding protein binding, protein tyrosine kinase activity, voltage-gated calcium channel activity, ubiquitin protein ligase activity, adenosine triphosphate (ATP) binding, and protein serine/threonine kinase. Additionally, prominent molecular pathways for each HD stage were then obtained, and metabolites related to each pathway for both disease stages were identified. The transforming growth factor beta (TGF-β) signaling (pre-symptomatic and symptomatic stages of the disease), calcium (Ca2+) signaling (pre-symptomatic), dopaminergic synapse pathway (symptomatic HD patients) and Hippo signaling (pre-symptomatic) pathways were identified. The in silico metabolites we identified include Ca2+, inositol 1,4,5-trisphosphate, sphingosine 1-phosphate, dopamine, homovanillate and L-tyrosine. The genes, pathways and metabolites identified for each HD stage can provide a better understanding of the mechanisms that become altered in each disease stage. Our results can guide the development of therapies that may target the altered genes and metabolites of the perturbed pathways, leading to an improvement in clinical symptoms and hopefully a delay in the age of onset.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is undeniably the most severe global health emergency since the 1918 Influenza outbreak. Depending on its evolutionary trajectory, the virus is expected to establish itself as an endemic infectious respiratory disease exhibiting seasonal flare-ups. Therefore, despite the unprecedented rally to reach a vaccine that can offer widespread immunization, it is equally important to reach effective prevention and treatment regimens for coronavirus disease 2019 (COVID-19). Contributing to this effort, we have curated and analyzed multi-source and multi-omics publicly available data from patients, cell lines and databases in order to fuel a multiplex computational drug repurposing approach. We devised a network-based integration of multi-omic data to prioritize the most important genes related to COVID-19 and subsequently re-rank the identified candidate drugs. Our approach resulted in a highly informed integrated drug shortlist by combining structural diversity filtering along with experts’ curation and drug–target mapping on the depicted molecular pathways. In addition to the recently proposed drugs that are already generating promising results such as dexamethasone and remdesivir, our list includes inhibitors of Src tyrosine kinase (bosutinib, dasatinib, cytarabine and saracatinib), which appear to be involved in multiple COVID-19 pathophysiological mechanisms. In addition, we highlight specific immunomodulators and anti-inflammatory drugs like dactolisib and methotrexate and inhibitors of histone deacetylase like hydroquinone and vorinostat with potential beneficial effects in their mechanisms of action. Overall, this multiplex drug repurposing approach, developed and utilized herein specifically for SARS-CoV-2, can offer a rapid mapping and drug prioritization against any pathogen-related disease.