Penicillium chrysogenum: Beyond the penicillin.

Almost one century after the Sir Alexander Fleming's fortuitous discovery of penicillin and the identification of the fungal producer as Penicillium notatum, later Penicillium chrysogenum (currently reidentified as Penicillium rubens), the molecular mechanisms behind the massive production of penicillin titers by industrial strains could be considered almost fully characterized. However, this filamentous fungus is not only circumscribed to penicillin, and instead, it seems to be full of surprises, thereby producing important metabolites and providing expanded biotechnological applications. This review, in addition to summarizing the classical role of P. chrysogenum as penicillin producer, highlights its ability to generate an array of additional bioactive secondary metabolites and enzymes, together with the use of this microorganism in relevant biotechnological processes, such as bioremediation, biocontrol, production of bioactive nanoparticles and compounds with pharmaceutical interest, revalorization of agricultural and food-derived wastes or the enhancement of food industrial processes and the agricultural production.

Penicillium chrysogenum as a fungal factory for feruloyl esterases.

Plant biomass is a promising substrate for biorefinery, as well as a source of bioactive compounds, platform chemicals, and precursors with multiple industrial applications. These applications depend on the hydrolysis of its recalcitrant structure. However, the effective biological degradation of plant cell walls requires several enzymatic groups acting synergistically, and novel enzymes are needed in order to achieve profitable industrial hydrolysis processes. In the present work, a feruloyl esterase (FAE) activity screening of Penicillium spp. strains revealed a promising candidate (Penicillium rubens Wisconsin 54-1255; previously Penicillium chrysogenum), where two FAE-ORFs were identified and subsequently overexpressed. Enzyme extracts were analyzed, confirming the presence of FAE activity in the respective gene products (PrFaeA and PrFaeB). PrFaeB-enriched enzyme extracts were used to determine the FAE activity optima (pH 5.0 and 50-55 °C) and perform proteome analysis by means of MALDI-TOF/TOF mass spectrometry. The studies were completed with the determination of other lignocellulolytic activities, an untargeted metabolite analysis, and upscaled FAE production in stirred tank reactors. The findings described in this work present P. rubens as a promising lignocellulolytic enzyme producer. KEY POINTS: • Two Penicillium rubens ORFs were first confirmed to have feruloyl esterase activity. • Overexpression of the ORFs produced a novel P. rubens strain with improved activity. • The first in-depth proteomic study of a P. rubens lignocellulolytic extract is shown.

Molecular Identification and Acid Stress Response of an Acidithiobacillus thiooxidans Strain Isolated from Rio Tinto (Spain).

Acidithiobacillus thiooxidans is of paramount importance in the development of biomining technologies. Being widely recognized as an extreme acidophile, extensive research has been dedicated to understanding its significant role in the extraction of several ores in recent years. However, there still exist significant molecular uncertainties surrounding this species. This study focuses on developing a taxonomic assignment method based on the sequencing of the 16S-5S rRNA cluster, along with a qPCR-based technology enabling precise growth determination. Additionally, an approach to understanding its response to acid stress is explored through RT-PCR and MALDI-TOF analysis. Our findings indicate that when subjected to pH levels below 1, the cell inhibits central (carbon fixation and metabolism) and energy (sulfur metabolism) metabolism, as well as chaperone synthesis, suggesting a potential cellular collapse. Nevertheless, the secretion of ammonia is enhanced to raise the environmental pH, while fatty acid synthesis is upregulated to reinforce the cell membrane.

A New Wavelet-Based Privatization Mechanism for Probability Distributions.

In this paper, we propose a new privatization mechanism based on a naive theory of a perturbation on a probability using wavelets, such as a noise perturbs the signal of a digital image sensor. Wavelets are employed to extract information from a wide range of types of data, including audio signals and images often related to sensors, as unstructured data. Specifically, the cumulative wavelet integral function is defined to build the perturbation on a probability with the help of this function. We show that an arbitrary distribution function additively perturbed is still a distribution function, which can be seen as a privatized distribution, with the privatization mechanism being a wavelet function. Thus, we offer a mathematical method for choosing a suitable probability distribution for data by starting from some guessed initial distribution. Examples of the proposed method are discussed. Computational experiments were carried out using a database-sensor and two related algorithms. Several knowledge areas can benefit from the new approach proposed in this investigation. The areas of artificial intelligence, machine learning, and deep learning constantly need techniques for data fitting, whose areas are closely related to sensors. Therefore, we believe that the proposed privatization mechanism is an important contribution to increasing the spectrum of existing techniques.

Comparative proteome analyses highlight several exercise-like responses of mouse sciatic nerve after IP injection of irisin.

Many beneficial effects of exercise on the nervous system are mediated by hormone (growth factor)/receptor signaling. Considering the accumulating evidence on the similarity of some beneficial effects, irisin can be a proposed effector of exercise; however, the mechanism underlying these effects remains largely unknown. More evidence on the mechanism of action might reveal its potential as a treatment strategy to substitute exercise recovery protocols for nerve injuries in physically disabled patients. To evaluate the underlying mechanism of irisin involvement in nerve adaptation and exerting beneficial effects, we studied the proteome profile alteration of mouse sciatic nerve after irisin administration. We also compared it with two 8-week protocols of resistance exercise and endurance exercise. The results indicate that irisin contributes to the regulation of nerve metabolism via overexpression of Ckm and ATP5j2 proteins. Irisin administration may improve sciatic nerve function by maintaining the architecture, enhancing axonal transport, and promoting synapse plasticity through increased structural and regulatory proteins and NO production. We also showed that irisin has the potential to induce neurotrophic support on the sciatic nerve by maintaining cell redox homeostasis, and responses to oxidative stress via the upregulation of disulfide-isomerase and superoxide dismutase enzymes. Comparing with exercise groups, these effects are somewhat exercise-like responses. These data suggest that irisin can be a promising therapeutic candidate for specific targeting of defects in peripheral neuropathies and nerve injuries as an alternative for physical therapy.

Irisin injection mimics exercise effects on the brain proteome.

Physical inactivity can endanger human health and increase the incidence of neurodegenerative disease. Exercise has tremendous beneficial effects on brain health and cognitive function, especially in older adults. It also improves brain-related outcomes in depression, epilepsy and neurodegenerative disorders, such as Parkinson's disease and Alzheimer's disease. Irisin is a mediator of the beneficial effects of exercise. This study aimed to assess the proteome alterations in adult male National Maritime Research Institute (NMRI) mice brain tissue upon three different conditions including endurance exercise, resistance exercise and irisin injection. Quantification of irisin levels in blood was performed using irisin-ELISA Kit. Quantification and identification of proteins via two-dimensional gel electrophoresis and matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry (MS)/MS showed the alteration of at least 21 proteins due to different treatments. Cellular pathway analysis revealed common beneficial effects of sole irisin treatment and different exercise procedures suggesting the capability of irisin injection to substitute the exercise when physical activity is not possible.

Cadmium uptake via apoplastic bypass flow in Oryza sativa.

It is known that rice roots take up cadmium (Cd) via the symplastic route mediated by membrane-bound mineral transporters. Here we provide evidence that apoplastic bypass flow is another Cd uptake route in rice. High concentrations of Cd rendered apoplastic bypass flow rate increased in rice seedlings. These concentrations of Cd compromised membrane integrity in the root meristem and transition zone. Polyethleneglycol and proline inhibited the Cd-induced apoplastic bypass flow and Cd transfer to the shoots. Loss-of-function mutant of the Cd uptake transporter, nramp5, showed Cd transport to the shoot comparable to the wild type. At a low Cd concentration, increased apoplastic bypass flow rate by NaCl stress resulted in an elevation of Cd transport to shoots both in the wildtype and nramp5. These observations indicate that apoplastic bypass flow in roots carries Cd transport leading to xylem loading of Cd in addition to the symplastic pathway mediated by mineral transporters under stressed conditions.

Lot-Size Models with Uncertain Demand Considering Its Skewness/Kurtosis and Stochastic Programming Applied to Hospital Pharmacy with Sensor-Related COVID-19 Data.

Governments have been challenged to provide timely medical care to face the COVID-19 pandemic. Under this pandemic, the demand for pharmaceutical products has changed significantly. Some of these products are in high demand, while, for others, their demand falls sharply. These changes in the random demand patterns are connected with changes in the skewness (asymmetry) and kurtosis of their data distribution. Such changes are critical to determining optimal lots and inventory costs. The lot-size model helps to make decisions based on probabilistic demand when calculating the optimal costs of supply using two-stage stochastic programming. The objective of this study is to evaluate how the skewness and kurtosis of the distribution of demand data, collected through sensors, affect the modeling of inventories of hospital pharmacy products helpful to treat COVID-19. The use of stochastic programming allows us to obtain results under demand uncertainty that are closer to reality. We carry out a simulation study to evaluate the performance of our methodology under different demand scenarios with diverse degrees of skewness and kurtosis. A case study in the field of hospital pharmacy with sensor-related COVID-19 data is also provided. An algorithm that permits us to use sensors when submitting requests for supplying pharmaceutical products in the hospital treatment of COVID-19 is designed. We show that the coefficients of skewness and kurtosis impact the total costs of inventory that involve order, purchase, holding, and shortage. We conclude that the asymmetry and kurtosis of the demand statistical distribution do not seem to affect the first-stage lot-size decisions. However, demand patterns with high positive skewness are related to significant increases in expected inventories on hand and shortage, increasing the costs of second-stage decisions. Thus, demand distributions that are highly asymmetrical to the right and leptokurtic favor high total costs in probabilistic lot-size systems.

A Two-Stage Location Problem with Order Solved Using a Lagrangian Algorithm and Stochastic Programming for a Potential Use in COVID-19 Vaccination Based on Sensor-Related Data.

Healthcare service centers must be sited in strategic locations that meet the immediate needs of patients. The current situation due to the COVID-19 pandemic makes this problem particularly relevant. Assume that each center corresponds to an assigned place for vaccination and that each center uses one or more vaccine brands/laboratories. Then, each patient could choose a center instead of another, because she/he may prefer the vaccine from a more reliable laboratory. This defines an order of preference that might depend on each patient who may not want to be vaccinated in a center where there are only her/his non-preferred vaccine brands. In countries where the vaccination process is considered successful, the order assigned by each patient to the vaccination centers is defined by incentives that local governments give to their population. These same incentives for foreign citizens are seen as a strategic decision to generate income from tourism. The simple plant/center location problem (SPLP) is a combinatorial approach that has been extensively studied. However, a less-known natural extension of it with order (SPLPO) has not been explored in the same depth. In this case, the size of the instances that can be solved is limited. The SPLPO considers an order of preference that patients have over a set of facilities to meet their demands. This order adds a new set of constraints in its formulation that increases the complexity of the problem to obtain an optimal solution. In this paper, we propose a new two-stage stochastic formulation for the SPLPO (2S-SPLPO) that mimics the mentioned pandemic situation, where the order of preference is treated as a random vector. We carry out computational experiments on simulated 2S-SPLPO instances to evaluate the performance of the new proposal. We apply an algorithm based on Lagrangian relaxation that has been shown to be efficient for large instances of the SPLPO. A potential application of this new algorithm to COVID-19 vaccination is discussed and explored based on sensor-related data. Two further algorithms are proposed to store the patient's records in a data warehouse and generate 2S-SPLPO instances using sensors.

A New Approach to Predicting Cryptocurrency Returns Based on the Gold Prices with Support Vector Machines during the COVID-19 Pandemic Using Sensor-Related Data.

In a real-world situation produced under COVID-19 scenarios, predicting cryptocurrency returns accurately can be challenging. Such a prediction may be helpful to the daily economic and financial market. Unlike forecasting the cryptocurrency returns, we propose a new approach to predict whether the return classification would be in the first, second, third quartile, or any quantile of the gold price the next day. In this paper, we employ the support vector machine (SVM) algorithm for exploring the predictability of financial returns for the six major digital currencies selected from the list of top ten cryptocurrencies based on data collected through sensors. These currencies are Binance Coin, Bitcoin, Cardano, Dogecoin, Ethereum, and Ripple. Our study considers the pre-COVID-19 and ongoing COVID-19 periods. An algorithm that allows updated data analysis, based on the use of a sensor in the database, is also proposed. The results show strong evidence that the SVM is a robust technique for devising profitable trading strategies and can provide accurate results before and during the current pandemic. Our findings may be helpful for different stakeholders in understanding the cryptocurrency dynamics and in making better investment decisions, especially under adverse conditions and during times of uncertain environments such as in the COVID-19 pandemic.

Disjoint and Functional Principal Component Analysis for Infected Cases and Deaths Due to COVID-19 in South American Countries with Sensor-Related Data.

In this paper, we group South American countries based on the number of infected cases and deaths due to COVID-19. The countries considered are: Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Peru, Paraguay, Uruguay, and Venezuela. The data used are collected from a database of Johns Hopkins University, an institution that is dedicated to sensing and monitoring the evolution of the COVID-19 pandemic. A statistical analysis, based on principal components with modern and recent techniques, is conducted. Initially, utilizing the correlation matrix, standard components and varimax rotations are calculated. Then, by using disjoint components and functional components, the countries are grouped. An algorithm that allows us to keep the principal component analysis updated with a sensor in the data warehouse is designed. As reported in the conclusions, this grouping changes depending on the number of components considered, the type of principal component (standard, disjoint or functional) and the variable to be considered (infected cases or deaths). The results obtained are compared to the k-means technique. The COVID-19 cases and their deaths vary in the different countries due to diverse reasons, as reported in the conclusions.

Regulation of the phosphate metabolism in Streptomyces genus: impact on the secondary metabolites.

The analysis of the inorganic phosphate effect over the antibiotics production is a long-distance history in the Streptomyces genus, which began almost at the same time that Michael Ende published his book entitled The Neverending Story. In some way, the unveiling of the pho regulon and its influence over the secondary metabolites production is an unfinished story, which keeps this subject as a trending topic, nowadays. Up to date, different studies have been releasing knowledge about particular areas of the pho regulon of different Streptomyces species. Nevertheless, for the first time, these knowledge drops are grouped in a review presenting a broad overview of the phosphate regulation and its impact over the secondary metabolites production in industrially relevant species. Even though the genetic response against phosphate scarcity is similar, as a whole, in different Streptomyces species, the fine-tuning is species-specific. Thus, the response regulator PhoP directly controls the secondary metabolites production in some species, whereas it regulates them in an indirect manner in other species. This information, unraveled in this review, is the result of the intensive analysis along last decade in several species of the genus that is allowing to distinguish how the phosphate response is unleashed in Streptomyces coelicolor, Streptomyces lividans, Streptomyces natalensis, Streptomyces lydicus, Streptomyces avermitilis, and Streptomyces tsukubaensis.

Analysis and validation of the pho regulon in the tacrolimus-producer strain Streptomyces tsukubaensis: differences with the model organism Streptomyces coelicolor.

Inorganic and organic phosphate controls both primary and secondary metabolism in Streptomyces genus. Metabolism regulation by phosphate in Streptomyces species is mediated by the PhoR-PhoP two-component system. Response regulator PhoP binds to conserved sequences of 11 nucleotides called direct repeat units (DRus), whose organization and conservation determine the binding of PhoP to distinct promoters. Streptomyces tsukubaensis is the industrial producer of the clinical immunosuppressant tacrolimus (FK506). A bioinformatic genome analysis detected several genes with conserved PHO boxes involved in phosphate scavenging and transport, nitrogen regulation, and secondary metabolite production. In this article, the PhoP regulation has been confirmed by electrophoretic mobility shift assays (EMSA) of the most relevant members of the traditional pho regulon such as the two-component system PhoR-P or genes involved in high-affinity phosphate transport (pstSCAB) and low-affinity phosphate transport (pit). However, the PhoP control over phosphatase genes in S. tsukubaensis is significantly different from the pattern reported in the model bacteria Streptomyces coelicolor. Thus, neither the alkaline phosphatase PhoA nor PhoD is regulated by PhoP. On the contrary, the binding of PhoP to the promoter of a novel putative phosphatase PhoX was confirmed. A crosstalk of the PhoP and GlnR regulators, which balances phosphate and nitrogen utilization, also occurs in S. tsukubaensis but slightly modified. Finally, PhoP regulates genes, like afsS, that link phosphate control and secondary metabolite production in S. tsukubaensis. In summary, there are notable differences between the regulation of specific genes of the pho regulon in S. tsukubaensis and the model organism S. coelicolor.

Trends in the biosynthesis and production of the immunosuppressant tacrolimus (FK506).

The current off-patent state of tacrolimus (FK506) has opened the hunting season for new generic pharmaceutical formulations of this immunosuppressant. This fact has boosted the scientific and industrial research on tacrolimus for the last 5 years in order to improve its production. The fast discovery of tacrolimus producer strains has generated a huge number of producers, which presents the biosynthetic cluster of FK506 as a high promiscuous genetic region. For the first time, the current state-of-the-art on the tacrolimus biosynthesis, production improvements and drug purification is reviewed. On one hand, all the genes involved in the tacrolimus biosynthesis, in addition to the traditional PKS/NRPS, as well as their regulation are analysed. On the other hand, tacrolimus direct and indirect precursors are reviewed as a straight manner to improve the final yield, which is a current trend in the field. Twenty years of industrial and scientific improvements on tacrolimus production are summarised, whereas future trends are also drafted.

The gamma-butyrolactone receptors BulR1 and BulR2 of Streptomyces tsukubaensis: tacrolimus (FK506) and butyrolactone synthetases production control.

Streptomyces tsukubaensis is a well-established industrial tacrolimus producer strain, but its molecular genetics is very poorly known. This information shortage prevents the development of tailored mutants in the regulatory pathways. A region (named bul) contains several genes involved in the synthesis and control of the gamma-butyrolactone autoregulator molecules. This region contains ten genes (bulA, bulZ, bulY, bulR2, bulS2, bulR1, bulW, bluB, bulS1, bulC) including two γ-butyrolactone receptor homologues (bulR1, bulR2), two putative gamma-butyrolactone synthetase homologues (bulS1, bulS2) and two SARP regulatory genes (bulY, bulZ). Analysis of the autoregulatory element (ARE)-like sequences by electrophoretic mobility shift assays and footprinting using the purified BulR1 and BulR2 recombinant proteins revealed six ARE regulatory sequences distributed along the bul cluster. These sequences showed specific binding of both BulR1 (the gamma-butyrolactone receptor) and BulR2, a possible pseudo γ-butyrolactone receptor. The protected region in all cases covered a 28-nt sequence with a palindromic structure. Optimal docking area analysis of BulR1 proved that this protein can be presented as either monomer or dimer but not oligomers and that it binds to the conserved ARE sequence in both strands. The effect on tacrolimus production was analysed by deletion of the bulR1 gene, which resulted in a strong decrease of tacrolimus production. Meanwhile, the ΔbulR2 mutation did not affect the biosynthesis of this immunosuppressant.

A six-compartment model for COVID-19 with transmission dynamics and public health strategies.

The global crisis of the COVID-19 pandemic has highlighted the need for mathematical models to inform public health strategies. The present study introduces a novel six-compartment epidemiological model that uniquely incorporates a higher isolation rate for unreported symptomatic cases of COVID-19 compared to reported cases, aiming to enhance prediction accuracy and address the challenge of initial underreporting. Additionally, we employ optimal control theory to assess the cost-effectiveness of interventions and adapt these strategies to specific epidemiological scenarios, such as varying transmission rates and the presence of asymptomatic carriers. By applying this model to COVID-19 data from India (30 January 2020 to 24 November 2020), chosen to capture the initial outbreak and subsequent waves, we calculate a basic reproduction number of 2.147, indicating the high transmissibility of the virus during this period in India. A sensitivity analysis reveals the critical impact of detection rates and isolation measures on disease progression, showing the robustness of our model in estimating the basic reproduction number. Through optimal control simulations, we demonstrate that increasing isolation rates for unreported cases and enhancing detection reduces the spread of COVID-19. Furthermore, our cost-effectiveness analysis establishes that a combined strategy of isolation and treatment is both more effective and economically viable. This research offers novel insights into the efficacy of non-pharmaceutical interventions, providing a tool for strategizing public health interventions and advancing our understanding of infectious disease dynamics.

An IoT-fuzzy intelligent approach for holistic management of COVID-19 patients.

In this study, an internet of things (IoT)-enabled fuzzy intelligent system is introduced for the remote monitoring, diagnosis, and prescription of treatment for patients with COVID-19. The main objective of the present study is to develop an integrated tool that combines IoT and fuzzy logic to provide timely healthcare and diagnosis within a smart framework. This system tracks patients' health by utilizing an Arduino microcontroller, a small and affordable computer that reads data from various sensors, to gather data. Once collected, the data are processed, analyzed, and transmitted to a web page for remote access via an IoT-compatible Wi-Fi module. In cases of emergencies, such as abnormal blood pressure, cardiac issues, glucose levels, or temperature, immediate action can be taken to monitor the health of critical COVID-19 patients in isolation. The system employs fuzzy logic to recommend medical treatments for patients. Sudden changes in these medical conditions are remotely reported through a web page to healthcare providers, relatives, or friends. This intelligent system assists healthcare professionals in making informed decisions based on the patient's condition.

Transcriptional analysis of the F0F1 ATPase operon of Corynebacterium glutamicum ATCC 13032 reveals strong induction by alkaline pH.

This article has been retracted at the request of Microbiology because identical bands for the 16S rRNA probe controls in the Northern blots were reported to correspond to experiments using different strains and experimental conditions in articles published in this journal and in Journal of Bacteriology over a period of 5 years.

Microarray studies reveal a 'differential response' to moderate or severe heat shock of the HrcA- and HspR-dependent systems in Corynebacterium glutamicum.

This article has been retracted at the request of Microbiology because identical bands for the 16S rRNA probe controls in the Northern blots were reported to correspond to experiments using different strains and experimental conditions in articles published in this journal and in Journal of Bacteriology over a period of 5 years.

Taxonomy and chemically semi-defined media for the analysis of the tacrolimus producer 'Streptomyces tsukubaensis'.

'Streptomyces tsukubaensis' was the first tacrolimus producer strain identified. Although it has been included in the Streptomyces genus, its taxonomic position has not been rigorously determined. By using a polyphasic approach, we have established that the tacrolimus producer strain 'S. tsukubaensis' NRRL 18488 represents a unique species in the Streptomyces genus, which is phylogenetically distant from other subsequently described producers. This fact means a horizontal transference of the tacrolimus-producing gene cluster. Physiology, nutrient requirement, and molecular genetics analyses of tacrolimus biosynthesis in 'S. tsukubaensis' necessitate chemically defined or semi-defined media, which work as a jigsaw puzzle and allow for pieces (nutrients) exchange. To date, studies related to 'S. tsukubaensis' have been mainly focused in the improvement of tacrolimus production using complex industrial fermentation media, which difficulty allows testing of tacrolimus overproduction enhancers or inhibitors because of the presence of non-defined substances. In the present work, two semi-defined media were developed in order to study the main factors involved in tacrolimus production in 'S. tsukubaensis'.