Identification and Genetic Characterization of Viral Pathogens in Ruminant Gestation Abnormalities,Israel, 2015–2019

Infectious agents including viruses are important abortifacients and can cause fetal abnormalities in livestock animals. Here, samples that had been collected in Israel from aborted or malformed ruminant fetuses between 2015 and 2019 were investigated for the presence of the following viruses: the reoviruses bluetongue virus (BTV) and epizootic hemorrhagic disease virus (EHDV), the flaviviruses bovine viral diarrhea virus (BVDV) and border disease virus (BDV), the peribunyaviruses Shuni virus (SHUV) and Akabane virus (AKAV), bovine herpesvirus type 1 (BoHV-1) and bovine ephemeral fever virus (BEFV). Domestic (cattle, sheep, goat) and wild/zoo ruminants were included in the study. The presence of viral nucleic acid or antigen could be confirmed in 21.8 % of abnormal pregnancies (213 out of 976 investigated cases), with peribunyaviruses, reoviruses and pestiviruses being the most prevalent. At least four different BTV serotypes were involved in abnormal courses of pregnancy in Israel. The subtyping of pestiviruses revealed the presence of two BDV and several distinct BVDV type 1 strains. The peribunyaviruses AKAV and SHUV were identified annually throughout the study period, however, variation in the extent of virus circulation could be observed between the years. In 2018, AKAV even represented the most detected pathogen in cases of small domestic ruminant gestation abnormalities. In conclusion, it was shown that various viruses are involved in abnormal courses of pregnancy in ruminants in Israel.     

Aberrant TIMP-1 production in tumor-associated fibroblasts drives the selective benefits of nintedanib in lung adenocarcinoma

The fibrotic tumor microenvironment is a pivotal therapeutic target. Nintedanib, a clinically approved multikinase antifibrotic inhibitor, is effective against lung adenocarcinoma (ADC) but not squamous cell carcinoma (SCC). Previous studies have implicated the secretome of tumor-associated fibroblasts (TAFs) in the selective effects of nintedanib in ADC, but the driving factor(s) remained unidentified. Here we examined the role of tissue inhibitor of metalloproteinase-1 (TIMP-1), a tumor-promoting cytokine overproduced in ADC-TAFs. To this aim, we combined genetic approaches with in vitro and in vivo preclinical models based on patient-derived TAFs. Nintedanib reduced TIMP-1 production more efficiently in ADC-TAFs than SCC-TAFs through a SMAD3-dependent mechanism. Cell culture experiments indicated that silencing TIMP1 in ADC-TAFs abolished the therapeutic effects of nintedanib on cancer cell growth and invasion, which were otherwise enhanced by the TAF secretome. Consistently, co-injecting ADC cells with TIMP1-knockdown ADC-TAFs into immunocompromised mice elicited a less effective reduction of tumor growth and invasion under nintedanib treatment compared to tumors bearing unmodified fibroblasts. Our results unveil a key mechanism underlying the selective mode of action of nintedanib in ADC based on the excessive production of TIMP-1 in ADC-TAFs. We further pinpoint reduced SMAD3 expression and consequent limited TIMP-1 production in SCC-TAFs as key for the resistance of SCC to nintedanib. These observations strongly support the emerging role of TIMP-1 as a critical regulator of therapy response in solid tumors.     

Beyond Cases and Deaths: The Benefits of Auxiliary Data Streams In Tracking the COVID-19 Pandemic: Can auxiliary indicators improve COVID-19 forecasting and hotspot prediction?

Short-term forecasts of traditional streams from public health reporting (such as cases, hospitalizations, and deaths) are a key input to public health decision-making during a pandemic. Since early 2020, our research group has worked with data partners to collect, curate, and make publicly available numerous real-time COVID-19 indicators, providing multiple views of pandemic activity in the United States. This paper studies the utility of five such indicators—derived from deidentified medical insurance claims, self-reported symptoms from online surveys, and COVID-related Google search activity—from a forecasting perspective. For each indicator, we ask whether its inclusion in an autoregressive (AR) model leads to improved predictive accuracy relative to the same model excluding it. Such an AR model, without external features, is already competitive with many top COVID-19 forecasting models in use today. Our analysis reveals that 1) inclusion of each of these five indicators improves on the overall predictive accuracy of the AR model; 2) predictive gains are in general most pronounced during times in which COVID cases are trending in “flat” or “down” directions; and 3) one indicator, based on Google searches, seems to be particularly helpful during “up” trends.

Tracking and forecasting indicators from public health reporting streams—such as confirmed cases and deaths in the COVID-19 pandemic—are crucial for understanding disease spread, correctly formulating public policy responses, and rationally planning future public health resource needs. A companion paper (1) describes our research group’s efforts, beginning in April 2020, in curating and maintaining a database of real-time indicators that track COVID-19 activity and other relevant phenomena. The signals (a term we use synonymously with “indicators”) in this database are accessible through the COVIDcast Application Programming Interface (API) (2), as well as associated R (3) and Python (4) packages, for convenient data fetching and processing. In the current paper, we quantify the utility provided by a core set of these indicators for two fundamental prediction tasks: probabilistic forecasting of COVID-19 case rates and prediction of future COVID-19 case hotspots (defined by the event that a relative increase in COVID-19 cases exceeds a certain threshold).At the outset, we should be clear that our intent in this paper is not to provide an authoritative take on cutting-edge COVID-19 forecasting methods. Similarly, some authors, e.g., ref. 5, have pointed out numerous mishaps of forecasting during the pandemic, and it is not our general intent to fix them here. Instead, we start with a basic and yet reasonably effective predictive model for future trends in COVID-19 cases and present a rigorous, quantitative assessment of the added value provided by auxiliary indicators that are derived from data sources that operate outside of traditional public health streams. In particular, we consider five indicators derived from deidentified medical insurance claims, self-reported symptoms from online surveys, and COVID-related Google searches.To assess this value in as direct terms as possible, we base our study around a very simple basic model: an autoregressive model, in which COVID cases in the near future are predicted using a linear combination of COVID cases in the near past. Forecasting carries a rich literature, offering a wide range of sophisticated techniques (see, e.g., ref. 6 for a review); however, we purposely avoid enhancements such as order selection, correction of outliers/anomalies in the data, and inclusion of regularization or nonlinearities. Similarly, we do not account for other factors that may well aid in forecasting, such as age-specific effects, holiday adjustments, and the effects of public health mandates. All that said, despite its simplicity, the basic autoregressive model that we consider in this paper exhibits competitive performance (see SI Appendix for details) with many of the top COVID-19 case forecasters submitted to the US COVID-19 Forecast Hub (7), which is the official source of forecasts used in public communications by the US Centers for Disease Control and Prevention (CDC). The strong performance of the autoregressive model here is in line with the fact that simple, robust models have also consistently been among the best-performing ones for COVID-19 death forecasting (8).In the companion paper (1), we analyze correlations between various indicators and COVID case rates. These correlations are natural summaries of the contemporaneous association between an indicator and COVID cases, but they fall short of delivering a satisfactory answer to the question that motivates the current article: Is the information contained in an indicator demonstrably useful for the prediction tasks we care about? Note that even lagged correlations cannot deliver a complete answer. Demonstrating utility for prediction is a much higher standard than simply asking about correlations; to be useful in forecast or hotspot models, an indicator must provide relevant information that is not otherwise contained in past values of the case rate series itself [cf. the pioneering work on Granger causality (9, 10), as well as the further references given below]. We assess this directly by inspecting the difference in predictive performance of simple autoregressive models trained with and without access to past values of a particular indicator.We find that each of the five indicators we consider—three based on COVID-related outpatient visits from medical insurance claims, one on self-reported symptoms from online surveys, and one on Google searches for anosmia or ageusia—provide an overall improvement in accuracy when incorporated into the autorgressive model. This is true both for COVID-19 case forecasting and for hotspot prediction. Further analysis reveals that the gains in accuracy depend on the pandemic’s dynamics at prediction time: The biggest gains in accuracy appear during times in which cases are “flat” or trending “down”; but the indicator based on Google searches offers a notable improvement when cases are trending “up.”Careful handling of data revisions plays a key role in our analysis. Signals computed from surveillance streams are often subject to latency and/or revision. For example, a signal based on aggregated medical insurance claims may be available after just a few days, but it can then be substantially revised over the next several weeks as additional claims are submitted and/or processed late. Correlations between such a signal and case rates calculated “after the fact” (i.e., computed retrospectively, using the finalized values of this signal) will not deliver an honest answer to the question of whether this signal would have been useful in real time. Instead, we build predictive models using only the data that would have been available as of the prediction date and compare the ensuing predictions in terms of accuracy. The necessity of real-time data for honest forecast evaluations has been recognized in econometrics for a long time (11–21), but it is often overlooked in epidemic forecasting despite its critical importance (22).Finally, it is worth noting that examining the importance of additional features for prediction is a core question in inferential statistics and econometrics, with work dating back to at least ref. 9. Still today, drawing rigorous inference based on predictions, without (or with lean) assumptions, is an active field of research from both applied and theoretical angles (23–32). Our take in the current work is in line with much of this literature; however, to avoid making any explicit assumptions, we do not attempt to make formal significance statements and, instead, broadly examine the stability of our conclusions with respect to numerous modes of analysis.     

The biosynthesis of thymol,carvacrol, and thymohydroquinone in Lamiaceae proceeds via cytochrome P450s and a short-chain dehydrogenase

Thymol and carvacrol are phenolic monoterpenes found in thyme, oregano, and several other species of the Lamiaceae. Long valued for their smell and taste, these substances also have antibacterial and anti-spasmolytic properties. They are also suggested to be precursors of thymohydroquinone and thymoquinone, monoterpenes with anti-inflammatory, antioxidant, and antitumor activities. Thymol and carvacrol biosynthesis has been proposed to proceed by the cyclization of geranyl diphosphate to γ-terpinene, followed by a series of oxidations via p-cymene. Here, we show that γ-terpinene is oxidized by cytochrome P450 monooxygenases (P450s) of the CYP71D subfamily to produce unstable cyclohexadienol intermediates, which are then dehydrogenated by a short-chain dehydrogenase/reductase (SDR) to the corresponding ketones. The subsequent formation of the aromatic compounds occurs via keto–enol tautomerisms. Combining these enzymes with γ-terpinene in in vitro assays or in vivo in Nicotiana benthamiana yielded thymol and carvacrol as products. In the absence of the SDRs, only p-cymene was formed by rearrangement of the cyclohexadienol intermediates. The nature of these unstable intermediates was inferred from reactions with the γ-terpinene isomer limonene and by analogy to reactions catalyzed by related enzymes. We also identified and characterized two P450s of the CYP76S and CYP736A subfamilies that catalyze the hydroxylation of thymol and carvacrol to thymohydroquinone when heterologously expressed in yeast and N. benthamiana. Our findings alter previous views of thymol and carvacrol formation, identify the enzymes involved in the biosynthesis of these phenolic monoterpenes and thymohydroquinone in the Lamiaceae, and provide targets for metabolic engineering of high-value terpenes in plants.

The phenolic monoterpenes of the Lamiaceae are widely used constituents of pharmaceuticals, cosmetics, and food products (1). Extracts of plants containing thymol or carvacrol are employed in medicine for their antibacterial, anti-spasmodic, antioxidant, and anti-cancer properties. Because of their pungent, warm, and aromatic odors, they also serve as additives to cosmetics and are used in aromatherapy. Thymol and carvacrol are best known as the aroma compounds of oregano and thyme, in which they provide the herbal, pizza-like tastes that are traditionally used in Mediterranean cuisine and food preservation (2). The occurrence of phenolic monoterpenes is restricted to a few genera in the Lamiaceae (Thymus, Origanum, Satureja, and Thymbra), Apiaceae (Trachyspermum), and Verbenaceae (Lippia). Of these, the essential oils of Thymus are the most important commercial source of phenolic monoterpenes (3). Thymus vulgaris L. and Origanum species also produce the structurally related monoterpenes thymohydroquinone and thymoquinone, which were first described in the essential oil of Nigella sativa L. black seed (4). Thymohydroquinone was shown to exhibit anti-cancer activity (5), and thymoquinone displays anti-inflammatory, hepatoprotective, antioxidant, cytotoxic, and anti-cancer activities (6).To date, only a few biosynthetic pathways to pharmaceutically valuable, oxidized terpenes have been completely elucidated, such as those leading to artemisinin, paclitaxel, and the phenolic, labdane-type diterpenes of sage and rosemary (7–9). For monoterpenes, a complex biosynthetic pathway has only been described for menthol and its derivatives in Mentha (10). However, the biosynthetic pathways for phenolic monoterpenes like thymol or carvacrol remain uncharacterized. Most monoterpenes are biosynthesized by fusion of the ubiquitous C₅ intermediates, isopentenyl diphosphate, and its isomer dimethylallyl diphosphate, resulting in the formation of a C₁₀ compound, geranyl diphosphate (GDP). This acyclic intermediate is the substrate for the large enzyme family of monoterpene synthases that form cyclic or acyclic products with an enormous variety of carbon skeletons (11, 12). In previous studies in thyme and oregano, the cyclic monoterpene olefin γ-terpinene was proposed as a precursor of thymol and carvacrol (Fig. 1A). Studies with ³H-labeled γ-terpinene showed that this compound was converted into thymol and carvacrol after incubation with young thyme leaves (13). Furthermore, the essential oils of plant species rich in either thymol or carvacrol have always been reported to contain substantial amounts of γ-terpinene (3). Terpene synthases forming γ-terpinene have been identified and characterized from various Lamiaceae species (14–19). In oregano, the expression of the γ-terpinene synthase OvTPS2 was found to correlate with thymol and carvacrol content in leaves (14). Beyond γ-terpinene, however, no further precursors of phenolic monoterpenes have been identified. The aromatic hydrocarbon p-cymene was suggested to be an intermediate in thymol and carvacrol formation from γ-terpinene (20), but its participation in the phenolic monoterpene pathway and the nature of the enzymes involved in formation of the aromatic ring still remain unknown. Moreover, there is little information about the conversion of the thymol and carvacrol to thymohydroquinone.Open in a separate windowFig. 1.T. vulgaris is composed of different monoterpene chemotypes. (A) Proposed biosynthetic pathway to the phenolic monoterpenes thymol and carvacrol as well as thymohydroquinone and thymoquinone. γ-terpinene and p-cymene were suggested as intermediates in the formation of thymol and carvacrol (17). (B) Essential oil composition of the T. vulgaris chemotypes dominated by carvacrol (C type), thymol (T type), and geraniol (G type). Terpenes were extracted with hexane and analyzed by GC-MS. The following terpenes were identified: 1, α-thujene; 2, α-pinene; 3, myrcene; 4, α-terpinene; 5, p-cymene; 6, γ-terpinene; 7, cis-sabinene hydrate; 8, linalool; 9, nerol; 10, neral; 11, thymoquinone; 12, geraniol; 13, geranial; 14, thymol; 15, carvacrol; 16, geranyl acetate; 17, (E)-β-caryophyllene; 18, thymohydroquinone; and 19, germacrene D. Nonyl acetate (10 µl/mL) was added as internal standard (IS) for quantification.In this study, we investigated the biosynthetic pathway leading to the formation of thymol, carvacrol, and thymohydroquinone in thyme and oregano. We isolated and characterized six cytochrome P450 monooxygenases (P450s) of the CYP71D subfamily from thyme and oregano accessions producing high levels of thymol and carvacrol. When these CYP genes were heterologously expressed and combined with a short-chain dehydrogenase from thyme in vitro or coexpressed in vivo in Nicotiana benthamiana, thymol or carvacrol were formed. Based on the characteristics of the expressed enzymes and their reaction with other substrates, we constructed the biosynthetic pathway leading to thymol and inferred the nature of unstable intermediates. Furthermore, we identified and characterized two P450s of the CYP76S and CYP736A subfamilies that hydroxylate thymol and carvacrol to thymohydroquinone when expressed in vivo in yeast and in N. benthamiana.     

Dielectric relaxation and relationships of local molecular mobility in linear and comb-like thermotropic polymers

The dielectric behavior of several thermotropic linear and comb-like polymers with flexible spacers and mesogenic fragments with different structure has been studied in a frequency range from 60 Hz to 1 MHz in the bulk state (below the glass transition temperature). Two regions of dipole relaxation caused by local mobility were detected. It was shown that the relaxation parameters of the first process virtually do not change with increasing spacer length and have similar values in polymers with different structure. In the case of the second process, in increase in spacer length considerably increases the mobility of kinetic elements.     

Effect of Ceramic Formation on the Emission of Eu3+ and Nd3+ Ions in Double Perovskites

Herein, the structure, morphology, as well as optical properties of the powder and ceramic samples of Ba₂MgWO₆ are presented. Powder samples were obtained by high temperature solid-state reaction, while, for the ceramics, the SPS technique under 50-MPa pressure was applied. The morphology of the investigated samples showed some agglomeration and grains with a submicron size of 490–492 µm. The theoretical density and relative density of ceramics were calculated using the Archimedes method. The influence of sample preparation on the position, shape, and character of the host, as well as dopants emission was investigated. Sample sintering enhances regular emission of WO₆ groups causing a blue shift of Ba₂MgWO₆ emission. Nonetheless, under X-ray excitation, only the green emission of inversion WO₆ group was detected. For the ceramic doped with Eu³⁺ ions, the emission of both host and dopant was detected. However, for the powder efficient host to activator energy, the transfer process occurred, and only the magnetic dipole emission of Eu³⁺ was detected. The intensity of Nd³⁺ ions of Ba₂MgWO₆ powder sample is five times higher than for the ceramic. The sintering process reduces inversion defects and creates a highly symmetrical site of neodymium ions. The emission of Ba₂MgWO₆:Nd³⁺ consists of transitions from the ⁴F_3/2 excited level to the ⁴I_J multiplet states with the dominance of the ⁴F_3/2→⁴I_11/2 one. The spectroscopic quality parameter and branching ratio of Nd³⁺ emission are presented.     

Reorientation of prostanoid production accompanies “activation” of adult microglial cells in culture

Using morphological, immunocytochemical, and functional parameters we have previously shown that highly purified adult rat microglial cells undergo a process of “activation” when cultured in a serum-containing medium in the absence of added proinflammatory substances or other factors (Slepko and Levi: Glia 16:241–246, 1996). Here we studied the lipopolysaccharide (LPS)-evoked production of two prostanoids, thromboxane A₂ (measured as thromboxane B₂) (TXB₂) and prostaglandin E₂ (PGE₂), as a function of microglial “activation.” LPS induced a greater time- and dose-dependent release of TXB₂, compared to PGE₂, in the less “activated” cells. Further “activation” led to amplified synthesis of PGE₂ and not of TXB₂, so that the TXB₂/PGE₂ ratio changed from 2.2 to 0.25 between the 2nd and 4th day in culture. Western blot experiments showed that the LPS-evoked expression of the inducible form of cyclooxygenase (COX) was markedly higher in cells exhibiting a more “activated” phenotype. The expression of the constitutive isoform of COX was low in all conditions, was slightly greater in more “activated” cells, and was not affected by LPS. Neither progression in microglial “activation” nor LPS treatment enhanced thromboxane synthase activity. We hypothesize that reorientation of prostanoid synthesis toward a major production of PGE₂ in the more “activated” cells can be largely attributed to an increased inducibility of cellular COX expression, combined with the inability of thromboxane synthase to cope with the increased availability of the COX product prostaglandin H₂ (PGH₂), the common precursor of TXA₂ and PGE₂. In view of the different, and at times opposite, functional activity of TXB₂ and PGE₂, the described change in prostanoid production pattern may contribute to the role of “activated” microglia in inflammation and host defense. J. Neurosci. Res. 49:292–300, 1997. © 1997 Wiley-Liss, Inc.     

Possible Arbovirus Found in Virome of Melophagus ovinus

Members of the Lipopteninae subfamily are blood-sucking ectoparasites of mammals. The sheep ked (Melophagus ovinus) is a widely distributed ectoparasite of sheep. It can be found in most sheep-rearing areas and can cause skin irritation, restlessness, anemia, weight loss and skin injuries. Various bacteria and some viruses have been detected in M. ovinus; however, the virome of this ked has never been studied using modern approaches. Here, we study the virome of M. ovinus collected in the Republic of Tuva, Russia. In our research, we were able to assemble full genomes for five novel viruses, related to the Rhabdoviridae (Sigmavirus), Iflaviridae, Reoviridae and Solemoviridae families. Four viruses were found in all five of the studied pools, while one virus was found in two pools. Phylogenetically, all of the novel viruses clustered together with various recently described arthropod viruses. All the discovered viruses were tested on their ability to replicate in the mammalian porcine embryo kidney (PEK) cell line. Aksy-Durug Melophagus sigmavirus RNA was detected in the PEK cell line cultural supernate after the first, second and third passages. Such data imply that this virus might be able to replicate in mammalian cells, and thus, can be considered as a possible arbovirus.