Fungal biotechnology

Fungi are used in many industrial processes, such as the production of enzymes, vitamins, polysaccharides, polyhydric alcohols, pigments, lipids, and glycolipids. Some of these products are produced commercially while others are potentially valuable in biotechnology. Fungal secondary metabolites are extremely important to our health and nutrition and have tremendous economic impact. In addition to the multiple reaction sequences of fermentations, fungi are extremely useful in carrying out biotransformation processes. These are becoming essential to the fine-chemical industry in the production of single-isomer intermediates. Recombinant DNA technology, which includes yeasts and other fungi as hosts, has markedly increased markets for microbial enzymes. Molecular manipulations have been added to mutational techniques as a means of increasing titers and yields of microbial processes and in the discovery of new drugs. Today, fungal biology is a major participant in global industry. Moreover, the best is yet to come as genomes of additional species are sequenced at some level (cDNA, complete genomes, expressed sequence tags) and gene and protein arrays become available

Computational identification of adaptive mutants using the VERT system

<p/> <p>Background</p> <p>Evolutionary dynamics of microbial organisms can now be visualized using the Visualizing Evolution in Real Time (VERT) system, in which several isogenic strains expressing different fluorescent proteins compete during adaptive evolution and are tracked using fluorescent cell sorting to construct a population history over time. Mutations conferring enhanced growth rates can be detected by observing changes in the fluorescent population proportions.</p> <p>Results</p> <p>Using data obtained from several VERT experiments, we construct a hidden Markov-derived model to detect these adaptive events in VERT experiments without external intervention beyond initial training. Analysis of annotated data revealed that the model achieves consensus with human annotation for 85-93% of the data points when detecting adaptive events. A method to determine the optimal time point to isolate adaptive mutants is also introduced.</p> <p>Conclusions</p> <p>The developed model offers a new way to monitor adaptive evolution experiments without the need for external intervention, thereby simplifying adaptive evolution efforts relying on population tracking. Future efforts to construct a fully automated system to isolate adaptive mutants may find the algorithm a useful tool.</p

Pth4, an ancient parathyroid hormone lost in eutherian mammals, reveals a new brain-to-bone signaling pathway

Regulation of bone development, growth, and remodeling traditionally has been thought to depend on endocrine and autocrine/paracrine modulators. Recently, however, brain-derived signals have emerged as key regulators of bone metabolism, although their mechanisms of action have been poorly understood. We reveal the existence of an ancient parathyroid hormone (Pth)4 in zebrafish that was secondarily lost in the eutherian mammals' lineage, including humans, and that is specifically expressed in neurons of the hypothalamus and appears to be a central neural regulator of bone development and mineral homeostasis. Transgenic fish lines enabled mapping of axonal projections leading from the hypothalamus to the brainstem and spinal cord. Targeted laser ablation demonstrated an essential role for of pth4-expressing neurons in larval bone mineralization. Moreover, we show that Runx2 is a direct regulator of pth4 expression and that Pth4 can activate cAMP signaling mediated by Pth receptors. Finally, gain-of-function experiments show that Pth4 can alter calcium/phosphorus levels and affect expression of genes involved in phosphate homeostasis. Based on our discovery and characterization of Pth4, we propose a model for evolution of bone homeostasis in the context of the vertebrate transition from an aquatic to a terrestrial lifestyle.Spanish Economy and Competitiveness Ministry Project [ALG2011-23581, AGL2014-52473R]; Portuguese Foundation for Science and Technology [PTDC/BIA-ANM/4225/2012-phos-fate]; U. S. National Institutes of Health/Office of the Director Grant [R01OD011116, R01 RR020833]; Generalitat de Catalunya [SGR2014-290]; Spanish Economy and Competitiveness Ministry [BFU2010-14875]; Science and Innovation Ministry [AGL2010-22247-C03-01]; Campus do Mar Ph.D. grant; Xunta de Galicia (Santiago, Spain) [AGL2014-52473R]info:eu-repo/semantics/publishedVersio

Bioinformatics challenges and potentialities in studying extreme environments

Cold environments are populated by organisms able to contravene deleterious effects of low temperature by diverse adaptive strategies, including the production of ice binding proteins (IBPs) that inhibit the growth of ice crystals inside and outside cells. We describe the properties of such a protein (EfcIBP) identified in the metagenome of an Antarctic biological consortium composed of the ciliate Euplotes focardii and psychrophilic non-cultured bacteria. Recombinant EfcIBP can resist freezing without any conformational damage and is moderately heat stable, with a midpoint temperature of 66.4 degrees C. Tested for its effects on ice, EfcIBP shows an unusual combination of properties not reported in other bacterial IBPs. First, it is one of the best-performing IBPs described to date in the inhibition of ice recrystallization, with effective concentrations in the nanomolar range. Moreover, EfcIBP has thermal hysteresis activity (0.53 degrees C at 50 mu M) and it can stop a crystal from growing when held at a constant temperature within the thermal hysteresis gap. EfcIBP protects purified proteins and bacterial cells from freezing damage when exposed to challenging temperatures. EfcIBP also possesses a potential N-terminal signal sequence for protein transport and a DUF3494 domain that is common to secreted IBPs. These features lead us to hypothesize that the protein is either anchored at the outer cell surface or concentrated around cells to provide survival advantage to the whole cell consortium

Biology and biotechnology of Trichoderma

Fungi of the genus Trichoderma are soilborne, green-spored ascomycetes that can be found all over the world. They have been studied with respect to various characteristics and applications and are known as successful colonizers of their habitats, efficiently fighting their competitors. Once established, they launch their potent degradative machinery for decomposition of the often heterogeneous substrate at hand. Therefore, distribution and phylogeny, defense mechanisms, beneficial as well as deleterious interaction with hosts, enzyme production and secretion, sexual development, and response to environmental conditions such as nutrients and light have been studied in great detail with many species of this genus, thus rendering Trichoderma one of the best studied fungi with the genome of three species currently available. Efficient biocontrol strains of the genus are being developed as promising biological fungicides, and their weaponry for this function also includes secondary metabolites with potential applications as novel antibiotics. The cellulases produced by Trichoderma reesei, the biotechnological workhorse of the genus, are important industrial products, especially with respect to production of second generation biofuels from cellulosic waste. Genetic engineering not only led to significant improvements in industrial processes but also to intriguing insights into the biology of these fungi and is now complemented by the availability of a sexual cycle in T. reesei/Hypocrea jecorina, which significantly facilitates both industrial and basic research. This review aims to give a broad overview on the qualities and versatility of the best studied Trichoderma species and to highlight intriguing findings as well as promising applications

Key Factors Associated With Pulmonary Sequelae in the Follow-Up of Critically Ill COVID-19 Patients

Introduction: Critical COVID-19 survivors have a high risk of respiratory sequelae. Therefore, we aimed to identify key factors associated with altered lung function and CT scan abnormalities at a follow-up visit in a cohort of critical COVID-19 survivors. Methods: Multicenter ambispective observational study in 52 Spanish intensive care units. Up to 1327 PCR-confirmed critical COVID-19 patients had sociodemographic, anthropometric, comorbidity and lifestyle characteristics collected at hospital admission; clinical and biological parameters throughout hospital stay; and, lung function and CT scan at a follow-up visit. Results: The median [p25–p75] time from discharge to follow-up was 3.57 [2.77–4.92] months. Median age was 60 [53–67] years, 27.8% women. The mean (SD) percentage of predicted diffusing lung capacity for carbon monoxide (DLCO) at follow-up was 72.02 (18.33)% predicted, with 66% of patients having DLCO < 80% and 24% having DLCO < 60%. CT scan showed persistent pulmonary infiltrates, fibrotic lesions, and emphysema in 33%, 25% and 6% of patients, respectively. Key variables associated with DLCO < 60% were chronic lung disease (CLD) (OR: 1.86 (1.18–2.92)), duration of invasive mechanical ventilation (IMV) (OR: 1.56 (1.37–1.77)), age (OR [per-1-SD] (95%CI): 1.39 (1.18–1.63)), urea (OR: 1.16 (0.97–1.39)) and estimated glomerular filtration rate at ICU admission (OR: 0.88 (0.73–1.06)). Bacterial pneumonia (1.62 (1.11–2.35)) and duration of ventilation (NIMV (1.23 (1.06–1.42), IMV (1.21 (1.01–1.45)) and prone positioning (1.17 (0.98–1.39)) were associated with fibrotic lesions. Conclusion: Age and CLD, reflecting patients’ baseline vulnerability, and markers of COVID-19 severity, such as duration of IMV and renal failure, were key factors associated with impaired DLCO and CT abnormalities

The evolution of the ventilatory ratio is a prognostic factor in mechanically ventilated COVID-19 ARDS patients

Background: Mortality due to COVID-19 is high, especially in patients requiring mechanical ventilation. The purpose of the study is to investigate associations between mortality and variables measured during the first three days of mechanical ventilation in patients with COVID-19 intubated at ICU admission. Methods: Multicenter, observational, cohort study includes consecutive patients with COVID-19 admitted to 44 Spanish ICUs between February 25 and July 31, 2020, who required intubation at ICU admission and mechanical ventilation for more than three days. We collected demographic and clinical data prior to admission; information about clinical evolution at days 1 and 3 of mechanical ventilation; and outcomes. Results: Of the 2,095 patients with COVID-19 admitted to the ICU, 1,118 (53.3%) were intubated at day 1 and remained under mechanical ventilation at day three. From days 1 to 3, PaO2/FiO2 increased from 115.6 [80.0-171.2] to 180.0 [135.4-227.9] mmHg and the ventilatory ratio from 1.73 [1.33-2.25] to 1.96 [1.61-2.40]. In-hospital mortality was 38.7%. A higher increase between ICU admission and day 3 in the ventilatory ratio (OR 1.04 [CI 1.01-1.07], p = 0.030) and creatinine levels (OR 1.05 [CI 1.01-1.09], p = 0.005) and a lower increase in platelet counts (OR 0.96 [CI 0.93-1.00], p = 0.037) were independently associated with a higher risk of death. No association between mortality and the PaO2/FiO2 variation was observed (OR 0.99 [CI 0.95 to 1.02], p = 0.47). Conclusions: Higher ventilatory ratio and its increase at day 3 is associated with mortality in patients with COVID-19 receiving mechanical ventilation at ICU admission. No association was found in the PaO2/FiO2 variation

Clustering COVID-19 ARDS patients through the first days of ICU admission. An analysis of the CIBERESUCICOVID Cohort

Background Acute respiratory distress syndrome (ARDS) can be classified into sub-phenotypes according to different inflammatory/clinical status. Prognostic enrichment was achieved by grouping patients into hypoinflammatory or hyperinflammatory sub-phenotypes, even though the time of analysis may change the classification according to treatment response or disease evolution. We aimed to evaluate when patients can be clustered in more than 1 group, and how they may change the clustering of patients using data of baseline or day 3, and the prognosis of patients according to their evolution by changing or not the cluster.Methods Multicenter, observational prospective, and retrospective study of patients admitted due to ARDS related to COVID-19 infection in Spain. Patients were grouped according to a clustering mixed-type data algorithm (k-prototypes) using continuous and categorical readily available variables at baseline and day 3.Results Of 6205 patients, 3743 (60%) were included in the study. According to silhouette analysis, patients were grouped in two clusters. At baseline, 1402 (37%) patients were included in cluster 1 and 2341(63%) in cluster 2. On day 3, 1557(42%) patients were included in cluster 1 and 2086 (57%) in cluster 2. The patients included in cluster 2 were older and more frequently hypertensive and had a higher prevalence of shock, organ dysfunction, inflammatory biomarkers, and worst respiratory indexes at both time points. The 90-day mortality was higher in cluster 2 at both clustering processes (43.8% [n = 1025] versus 27.3% [n = 383] at baseline, and 49% [n = 1023] versus 20.6% [n = 321] on day 3). Four hundred and fifty-eight (33%) patients clustered in the first group were clustered in the second group on day 3. In contrast, 638 (27%) patients clustered in the second group were clustered in the first group on day 3.Conclusions During the first days, patients can be clustered into two groups and the process of clustering patients may change as they continue to evolve. This means that despite a vast majority of patients remaining in the same cluster, a minority reaching 33% of patients analyzed may be re-categorized into different clusters based on their progress. Such changes can significantly impact their prognosis