Emerging Molecular Tools for Engineering Phytomicrobiome

Microbial plant interaction plays a major role in the sustainability of plants. The understanding of phytomicrobiome interactions enables the gene-editing tools for the construction of the microbial consortia. In this interaction, microbes share several common secondary metabolites and terpenoid metabolic pathways with their host plants that ensure a direct connection between the microbiome and associated plant metabolome. In this way, the CRISPR-mediated gene-editing tool provides an attractive approach to accomplish the creation of microbial consortia. On the other hand, the genetic manipulation of the host plant with the help of CRISPR-Cas9 can facilitate the characterization and identification of the genetic determinants. It leads to the enhancement of microbial capacity for more trait improvement. Many plant characteristics like phytovolatilization, phytoextraction, phytodesalination and phytodegradation are targeted by these approaches. Alternatively, chemical communications by PGPB are accomplished by the exchange of different signal molecules. For example, quorum-sensing is the way of the cell to cell communication in bacteria that lead to the detection of metabolites produced by pathogens during adverse conditions and also helpful in devising some tactics towards understanding plant immunity. Along with quorum-sensing, different volatile organic compounds and N-acyl homoserine lactones play a significant role in cell to cell communication by microbe to plant and among the plants respectively. Therefore, it is necessary to get details of all the significant approaches that are useful in exploring cell to cell communications. In this review, we have described gene-editing tools and the cell to cell communication process by quorum-sensing based signaling. These signaling processes via CRISPR- Cas9 mediated gene editing can improve the microbe-plant community in adverse climatic conditions.     

A Permian nurse log and evidence for facilitation in high‐latitude Glossopteris forests

The biology of trees that grew in high‐latitude forests during warmer geological periods is of major interest in understanding past and future ecosystem dynamics. As we study the different plants that composed these forests, it becomes possible to make comparisons with ecosystem processes that occur today. Here we describe a silicified late Permian (Lopingian) glossopterid (seed fern) trunk from Skaar Ridge, central Transantarctic Mountains, Antarctica, with evidence of glossopterid rootlets growing into its wood. The specimen is interpreted as a nurse log similar to those seen in some extant forests. Together with evidence of glossopterid roots growing within the lacunae of older roots, this new specimen suggests the existence of facilitative interactions among the glossopterid trees that dominated the high‐latitude forests of Gondwana during the late Permian. More generally, the existence of self‐facilitation might have favoured the expansion of glossopterids within various environments, especially those at high palaeolatitudes, during the Permian icehouse to greenhouse transition.     

Large-scale pharmacogenomic studies and drug response prediction for personalized cancer medicine

The response rate of most anti-cancer drugs is limited because of the high heterogeneity of cancer and the complex mechanism of drug action. Personalized treatment that stratifies patients into subgroups using molecular biomarkers is promising to improve clinical benefit. With the accumulation of preclinical models and advances in computational approaches of drug response prediction, pharmacogenomics has made great success over the last 20 years and is increasingly used in the clinical practice of personalized cancer medicine. In this article, we first summarize FDA-approved pharmacogenomic biomarkers and large-scale pharmacogenomic studies of preclinical cancer models such as patient-derived cell lines, organoids, and xenografts. Furthermore, we comprehensively review the recent developments of computational methods in drug response prediction, covering network, machine learning, and deep learning technologies and strategies to evaluate immunotherapy response. In the end, we discuss challenges and propose possible solutions for further improvement.     

Plasma proteins δ15N vs plasma urea as candidate biomarkers of between-animal variations of feed efficiency in beef cattle: Phenotypic and genetic evaluation

Identifying animals that are superior in terms of feed efficiency may improve the profitability and sustainability of the beef cattle sector. However, measuring feed efficiency is costly and time-consuming. Biomarkers should thus be explored and validated to predict between-animal variation of feed efficiency for both genetic selection and precision feeding. In this work, we aimed to assess and validate two previously identified biomarkers of nitrogen (N) use efficiency in ruminants, plasma urea concentrations and the ¹⁵N natural abundance in plasma proteins (plasma δ¹⁵N), to predict the between-animal variation in feed efficiency when animals were fed two contrasted diets (high-starch vs high-fibre diets). We used an experimental network design with a total of 588 young bulls tested for feed efficiency through two different traits (feed conversion efficiency [FCE] and residual feed intake [RFI]) during at least 6 months in 12 cohorts (farm × period combination). Animals reared in the same cohort, receiving the same diet and housed in the same pen, were considered as a contemporary group (CG). To analyse between-animal variations and explore relationships between biomarkers and feed efficiency, two statistical approaches, based either on mixed-effect models or regressions from residuals, were conducted to remove the between-CG variability. Between-animal variation of plasma δ¹⁵N was significantly correlated with feed efficiency measured through the two criteria traits and regardless of the statistical approach. Conversely, plasma urea was not correlated to FCE and showed only a weak, although significant, correlation with RFI. The response of plasma δ¹⁵N to FCE variations was higher when animals were fed a high-starch compared to a high-fibre diet. In addition, we identified two dietary factors, the metabolisable protein to net energy ratio and the rumen protein balance that influenced the relation between plasma δ¹⁵N and FCE variations. Concerning the genetic evaluation, and despite the moderate heritability of the two biomarkers (0.28), the size of our experimental setup was insufficient to detect significant genetic correlations between feed efficiency and the biomarkers. However, we validated the potential of plasma δ¹⁵N to phenotypically discriminate two animals reared in identical conditions in terms of feed efficiency as long as they differ by at least 0.049 g/g for FCE and 1.67 kg/d for RFI. Altogether, the study showed phenotypic, but non-genetic, relationships between plasma proteins δ¹⁵N and feed efficiency that varied according to the efficiency index and the diet utilised.     

Early social contact alters the community structure and functions of the faecal microbiome in suckling-growing piglets

Social contact during suckling, in an enriched social environment, can reduce the aggressive behaviours of piglets during regrouping at weaning, and improve their production performance and welfare. The aim of this study was to determine the possible impact of suckling social contact on gut microbes. We performed 16S rRNA sequencing to measure the faecal microbial structure and function in piglets experiencing social contact. Eighteen-litter piglets were allocated to two treatments: an early continuous social contact (CSC) group where piglets from adjacent pens shared a mutual pen starting at 14 days postpartum and a control (CON) group where piglets had no contact with individuals from adjacent pens during the suckling period. The piglets were regrouped at 36 days of age. The litter weights at 35 and 63 days of age were measured. Faecal samples were randomly collected at 16, 35, 42, and 63 days of age and faecal DNA was determined. The results showed that the litter weight of piglets in the CSC group was significantly decreased at 63 days compared with the CON group. Continuous social contact also significantly decreased the microbial richness at 16 and 35 days of age (P < 0.05). Firmicutes was the most abundant bacterial phylum in both groups at all detected time-points and the abundance increased with social contact. At the genus level, Lactobacillus was the most abundant bacterium after weaning and the abundance increased in the piglets with social contact. Compared with the faecal microbiota of control piglets, a total of 22 genera at 16 days, 20 genera at 35 days, 12 genera at 42 days, and 27 genera at 63 days in the faeces of CSC piglets were observed to be significantly different in abundance (linear discriminant analysis score > 3, P < 0.05). Furthermore, functional analysis of the microbial composition showed that the changes induced by early CSC mainly altered the relative abundance of metabolic and related pathways. The social contact notably had an effect on the abundance of microbial pathways for amino acid and carbohydrate metabolism. In conclusion, CSC changed the microbial composition in the faeces of piglets, which might have a negative effect on nutrient metabolism for the suckling-growing piglets. Our study provided new insight into the influence of social contact on the suckling-growing piglets.     

Design of mutualistic microbial consortia for stable conversion of carbon monoxide to value-added chemicals

Carbon monoxide (CO) is a promising carbon source for producing value-added biochemicals via microbial fermentation. However, its microbial conversion has been challenging because of difficulties in genetic engineering of CO-utilizing microorganisms and, more importantly, maintaining CO consumption which is negatively affected by the toxicity of CO and accumulated byproducts. To overcome these issues, we devised mutualistic microbial consortia, co-culturing Eubacterium limosum and genetically engineered Escherichia coli for the production of 3-hydroxypropionic acid (3-HP) and itaconic acid (ITA). During the co-culture, E. limosum assimilated CO and produced acetate, a toxic by-product, while E. coli utilized acetate as a sole carbon source. We found that this mutualistic interaction dramatically stabilized and improved CO consumption of E. limosum compared to monoculture. Consequently, the improved CO consumption allowed successful production of 3-HP and ITA from CO. This study is the first demonstration of value-added biochemical production from CO using a microbial consortium. Moreover, it suggests that synthetic mutualistic microbial consortium can serve as a powerful platform for the valorization of CO.     

Degree of bacterial contamination in barbershops using hair dryers in Riyadh

Barbershops provide areas for the growth and transfer of bacterial pathogens and thereby have an impact on public health. Barbershops are ideal places for the interactive spread of infections, including community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA). Here, the work determines the degree of bacterial contamination of hair dryers used in barbershops. The samples were collected in the city of Riyadh, the Kingdom of Saudi Arabia on March 2019. Significant bacterial contamination was seen, with total bacterial count increasing when the hair dryers were run for 20 instead of 10 s. The study shows a high level of bacterial contamination barbershops using hair dryers, with MRSA being isolated in some. The results suggest that high quality filters should be used inside hair dryers and filters, and theses should be cleaned frequently.     

Floral fungal-bacterial community structure and co-occurrence patterns in four sympatric island plant species

Flowers’ fungal and bacterial communities can exert great impacts on host plant wellness and reproductive success—both directly and indirectly through species interactions. However, information about community structure and co-occurrence patterns in floral microbiome remains scarce. Here, using culture-independent methods, we investigated fungal and bacterial communities associated with stamens and pistils of four plant species (Scaevola taccada, Ipomoea cairica, Ipomoea pes-caprae, and Mussaenda kwangtungensis) growing together under the same environment conditions in an island located in South China. Plant species identity significantly influenced community composition of floral fungi but not bacteria. Stamen and pistil microbiomes did not differ in community composition, but differed in co-occurrence network topological features. Compared with the stamen network, pistil counterpart had fewer links between bacteria and fungi and showed more modular but less concentrated and connected structure. In addition, degree distribution of microbial network in each host species and each microhabitat (stamen or pistil) followed a significant power-law pattern. These results enhance our understanding in the assembly principles and ecological interactions of floral microbial communities.     

Studies toward the comprehension of fungal-macroalgae interaction in cold marine regions from a biotechnological perspective

In marine ecosystems, macroalgae are the habitat for several microorganisms, fungi being among them. In the Antarctic benthic coastal ecosystem, macroalgae play a key role in organic matter cycling. In this study, 13 different macroalgae from Potter Cove and surrounding areas were sampled and 48 fungal isolates were obtained from six species, four Rhodophyta Ballia callitricha, Gigartina skottsbergii, Neuroglossum delesseriae and Palmaria decipiens, and two Phaeophyceae: Adenocystis utricularis and Ascoseira mirabilis. Fungal isolates mostly belonged to the Ascomycota phylum (Antarctomyces, Cadophora, Cladosporium, Penicillium, Phialocephala, and Pseudogymnoascus) and only one to the phylum Mucoromycota. Two of the isolates could not be identified to genus level, implying that Antarctica is a source of probable novel fungal taxa with enormous bioprospecting and biotechnological potential. 73% of the fungal isolates were moderate eurypsychrophilic (they grew at 5–25 °C), 12.5% were eurypsychrophilic and grew in the whole range, 12.5% of the isolates were narrow eurypsychrophilic (growth at 15–25 °C), and Mucoromycota AUe4 was classified as stenopsychrophilic as it grew at 5–15 °C. Organic extracts of seven macroalgae from which no fungal growth was obtained (three red algae Georgiella confluens, Gymnogongrus turquetii, Plocamium cartlagineum, and four brown algae Desmarestia anceps, D. Antarctica, Desmarestia menziesii, Himantothallus grandifolius) were tested against representative fungi of the genera isolated in this work. All extracts presented fungal inhibition, those from Plocamium cartilagineum and G. turquetii showed the best results, and for most of these macroalgae, this represents the first report of antifungal activity and constitute a promising source of compounds for future evaluation.     

Identification of Human Vital Functions Directly Relevant to the Respiratory System Based on the Cardiac and Acoustic Parameters and Random Forest

Regarding sleep research, polysomnography (PSG) also called a sleep study, is a gold standard. It incorporates brain waves, the oxygen level in the blood, heart rate and breathing, and leg movement recordings. PSG is a complicated and expensive laboratory-based procedure, usually done in hospitals or special sleep center. In this study, an alternative technique for Sleep-Related Breathing Disorders (SRBD) based on selected cardiac and acoustic parameters and the Random Forest (RF) has been studied. A system dedicated to the detection of simultaneously acquired ECG and acoustic signals, which are collected during sleep at home environment is proposed. Results obtained indicate that classification and regression tree models such as RF are appropriate for the evaluation of sleep disorders like SRBD. The best identification of sleep irregularities at level 89.00 percent for the raw database was obtained. Thus, statistical predictive models allow identification of breathing events with high levels of sensitivity and specificity, providing an inexpensive and accurate diagnosis.