Gut microbiome dynamics and predictive value in hospitalized COVID-19 patients: a comparative analysis of shallow and deep shotgun sequencing.

The COVID-19 pandemic caused by SARS-CoV-2 has led to a wide range of clinical presentations, with respiratory symptoms being common. However, emerging evidence suggests that the gastrointestinal (GI) tract is also affected, with angiotensin-converting enzyme 2, a key receptor for SARS-CoV-2, abundantly expressed in the ileum and colon. The virus has been detected in GI tissues and fecal samples, even in cases with negative results of the reverse transcription polymerase chain reaction in the respiratory tract. GI symptoms have been associated with an increased risk of ICU admission and mortality. The gut microbiome, a complex ecosystem of around 40 trillion bacteria, plays a crucial role in immunological and metabolic pathways. Dysbiosis of the gut microbiota, characterized by a loss of beneficial microbes and decreased microbial diversity, has been observed in COVID-19 patients, potentially contributing to disease severity. We conducted a comprehensive gut microbiome study in 204 hospitalized COVID-19 patients using both shallow and deep shotgun sequencing methods. We aimed to track microbiota composition changes induced by hospitalization, link these alterations to clinical procedures (antibiotics administration) and outcomes (ICU referral, survival), and assess the predictive potential of the gut microbiome for COVID-19 prognosis. Shallow shotgun sequencing was evaluated as a cost-effective diagnostic alternative for clinical settings. Our study demonstrated the diverse effects of various combinations of clinical parameters, microbiome profiles, and patient metadata on the precision of outcome prognostication in patients. It indicates that microbiological data possesses greater reliability in forecasting patient outcomes when contrasted with clinical data or metadata. Furthermore, we established that shallow shotgun sequencing presents a viable and cost-effective diagnostic alternative to deep sequencing within clinical environments.

An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture.

This review discusses the transformative potential of integrating multi-omics data and artificial intelligence (AI) in advancing horticultural research, specifically plant phenotyping. The traditional methods of plant phenotyping, while valuable, are limited in their ability to capture the complexity of plant biology. The advent of (meta-)genomics, (meta-)transcriptomics, proteomics, and metabolomics has provided an opportunity for a more comprehensive analysis. AI and machine learning (ML) techniques can effectively handle the complexity and volume of multi-omics data, providing meaningful interpretations and predictions. Reflecting the multidisciplinary nature of this area of research, in this review, readers will find a collection of state-of-the-art solutions that are key to the integration of multi-omics data and AI for phenotyping experiments in horticulture, including experimental design considerations with several technical and non-technical challenges, which are discussed along with potential solutions. The future prospects of this integration include precision horticulture, predictive breeding, improved disease and stress response management, sustainable crop management, and exploration of plant biodiversity. The integration of multi-omics and AI holds immense promise for revolutionizing horticultural research and applications, heralding a new era in plant phenotyping.

Universal mtDNA fragment for Cervidae barcoding species identification using phylogeny and preliminary analysis of machine learning approach.

The aim of the study was to use total DNA obtained from bone material to identify species of free-living animals based on the analysis of mtDNA fragments by molecular methods using accurate bioinformatics tools Bayesian approach and the machine learning approach. In our research, we present a case study of successful species identification based on degraded samples of bone, with the use of short mtDNA fragments. For better barcoding, we used molecular and bioinformatics methods. We obtained a partial sequence of the mitochondrial cytochrome b (Cytb) gene for Capreolus capreolus, Dama dama, and Cervus elaphus, that can be used for species affiliation. The new sequences have been deposited in GenBank, enriching the existing Cervidae mtDNA base. We have also analysed the effect of barcodes on species identification from the perspective of the machine learning approach. Machine learning approaches of BLOG and WEKA were compared with distance-based (TaxonDNA) and tree-based (NJ tree) methods based on the discrimination accuracy of the single barcodes. The results indicated that BLOG and WEKAs SMO classifier and NJ tree performed better than TaxonDNA in discriminating Cervidae species, with BLOG and WEKAs SMO classifier performing the best.

Molecular structure, comparative and phylogenetic analysis of the complete chloroplast genome sequences of weedy rye Secale cereale ssp. segetale.

The complete chloroplast genome of Secale cereale ssp. segetale (Zhuk.) Roshev. (Poaceae: Triticeae) was sequenced and analyzed to better use its genetic resources to enrich rye and wheat breeding. The study was carried out using the following methods: DNA extraction, sequencing, assembly and annotation, comparison with other complete chloroplast genomes of the five Secale species, and multigene phylogeny. As a result of the study, it was determined that the chloroplast genome is 137,042 base pair (bp) long and contains 137 genes, including 113 unique genes and 24 genes which are duplicated in the IRs. Moreover, a total of 29 SSRs were detected in the Secale cereale ssp. segetale chloroplast genome. The phylogenetic analysis showed that Secale cereale ssp. segetale appeared to share the highest degree of similarity with S. cereale and S. strictum. Intraspecific diversity has been observed between the published chloroplast genome sequences of S. cereale ssp. segetale. The genome can be accessed on GenBank with the accession number (OL688773).

Data Science and Plant Metabolomics.

The study of plant metabolism is one of the most complex tasks, mainly due to the huge amount and structural diversity of metabolites, as well as the fact that they react to changes in the environment and ultimately influence each other. Metabolic profiling is most often carried out using tools that include mass spectrometry (MS), which is one of the most powerful analytical methods. All this means that even when analyzing a single sample, we can obtain thousands of data. Data science has the potential to revolutionize our understanding of plant metabolism. This review demonstrates that machine learning, network analysis, and statistical modeling are some techniques being used to analyze large quantities of complex data that provide insights into plant development, growth, and how they interact with their environment. These findings could be key to improving crop yields, developing new forms of plant biotechnology, and understanding the relationship between plants and microbes. It is also necessary to consider the constraints that come with data science such as quality and availability of data, model complexity, and the need for deep knowledge of the subject in order to achieve reliable outcomes.

Nanopriming of Barley Seeds-A Shotgun Approach to Improve Germination under Salt Stress Conditions by Regulating of Reactive Oxygen Species.

Abiotic stresses are the most important environmental factors affecting seed germination, and negatively affect crop production worldwide. Water availability is essential for proper seed imbibition and germination. The mechanism by which seeds can germinate in areas with high soil salinity is, however, still unclear. The present study aims to investigate the protective roles of AgNPs in alleviating stress symptoms caused by salinity exposure in barley seeds. For this purpose, different treatment combinations of seed priming with PVP-AgNPs in salinity stress conditions were used. Salt stress (150 and 200 mM) was found to reduce seed germination by 100% when compared to the control. Under NaCl concentrations, seed priming with PVP-AgNPs (40 mg L-1) only for 2 h, reduced salinity effects. Salinity resulted in increased reactive oxygen species (ROS) generation compared to the control. However, increased antioxidants in the NPs treatments, such as SOD, CAT, GR, GPX (expression at both genes, such as HvSOD, HvCAT, HvGR or HvGPX, and protein levels) and glutathione content, scavenged these ROS. Considering all of the parameters under study, priming alleviated salt stress. To summarize, seed priming with AgNPs has the potential to alleviate salinity stress via reduced ROS generation and activation of the antioxidant enzymatic system during germination.

IoT in Water Quality Monitoring-Are We Really Here?

The Internet of Things (IoT) has become widespread. Mainly used in industry, it already penetrates into every sphere of private life. It is often associated with complex sensors and very complicated technology. IoT in life sciences has gained a lot of importance because it allows one to minimize the costs associated with field research, expeditions, and the transport of the many sensors necessary for physical and chemical measurements. In the literature, we can find many sensational ideas regarding the use of remote collection of environmental research. However, can we fully say that IoT is well established in the natural sciences?

Post-Effort Changes in Autophagy- and Inflammation-Related Gene Expression in White Blood Cells of Healthy Young Men.

Acute, strenuous physical exertion requiring high levels of energy production induces the production of reactive oxygen species and metabolic disturbances that can damage the mitochondria. Thus, selective autophagic elimination of defective mitochondria may improve resistance to oxidative stress and potentially to inflammation. The main goal of this study was to evaluate the impacts of intense effort on changes in the expression of select genes related to post-effort inflammation and autophagy. Thirty-five men aged 16-21 years were recruited to the study. The impacts of both aerobic (endurance) and anaerobic (speed) efforts on selected genes encoding chemokines (CXCL5, 8-12) were analyzed. Significant increases in the expression of all studied genes excluding CXCL12 were observed. Moreover, both types of effort induced an increase in the expression of genes encoding IL-2, -4, -6, -10, IFN-γ and TNF-α, excluding IL-17A. Generally, these efforts caused a significant increase in the relative expression of apoptosis- (BCL2 and BAX) and autophagy- (BNIP3, BECN1, MAP1LC3B, ATG5, ATG7, ATG12, ATG16L1 and SQSTM1) related genes. It seems that the duration of physical activity and its bioenergetic cost has an important impact on the degree of increase in expression of this panel of autophagy-related genes. Anaerobic effort is more strenuous than aerobic effort and requires a higher bioenergetic investment. This may explain the stronger impact of anaerobic effort on the expression of the studied genes. This observation seems to support the protective role of autophagy proposed in prior studies.

Tissue Printing and Dual Excitation Flow Cytometry for Oxidative Stress-New Tools for Reactive Oxygen Species Research in Seed Biology.

The intracellular homeostasis of reactive oxygen species (ROS) and especially of superoxide anion and hydrogen peroxide participate in signaling cascades which dictate developmental processes and reactions to stresses. ROS are also biological molecules that play important roles in seed dormancy and germination. Because of their rapid reactivity, short half-life and low concentration, ROS are difficult to measure directly with high accuracy and precision. In presented work tissue printing method with image analysis and dual excitation flow cytometry (FCM) were developed for rapid detection and localization of O2•- and H2O2 in different part of seed. Tissue printing and FCM detection of ROS showed that germination of wild oat seeds was associated with the accumulation of O2•- and H2O2 in embryo (coleorhiza, radicle and scutellum), aleurone layer and coat. To verify if printing and FCM signals were specified, the detection of O2•- and H2O2 in seeds incubated in presence of O2•- generation inhibitor (DPI) or H2O2 scavenger (CAT) were examined. All results were a high level of agreement among the level of ROS derived from presented procedures with the ones created from spectrophotometric measured data. In view of the data obtained, tissue printing with image analysis and FCM are recommended as a simple and fast methods, which could help researchers to detection and level determination of ROS in the external and inner parts of the seeds.

Involvement of ethylene biosynthesis and perception during germination of dormant Avena fatua L. caryopses induced by KAR1 or GA3.

MAIN CONCLUSION: Germination of primary dormant wild oat caused by KAR1 or GA3 is associated with ACC accumulation and increased ethylene production shortly before radicle protrusion as a result of the non-transcriptional and transcriptional activation of ACS and ACO enzymes, respectively. Response to both compounds involves the modulation of ethylene sensitivity through ethylene receptor genes. Harvested Avena fatua caryopses are primary dormant and, therefore, germinated poorly at 20 °C. Karrikin 1 (KAR1), which action probably requires endogenous gibberellins (GAs), and gibberellin A3 (GA3) was found to induce dormant caryopses to germinate. The stimulatory effects were accompanied by the activation of the ethylene biosynthesis pathway and depended on undisturbed ethylene perception. KAR1 and GA3 promoted 1-aminocyclopropane-1-carboxylic acid (ACC) accumulation during coleorhizae emergence and ethylene production shortly prior to the radicle protrusion, which resulted from the enhanced activity of two ethylene biosynthesis enzymes, ACC synthase (ACS) and ACC oxidase (ACO). The inhibitor of ACS adversely affected beneficial impacts of both KAR1 and GA3 on A. fatua caryopses germination, while the inhibitor of ACO more efficiently impeded the GA3 effect. The inhibitors of ethylene action markedly lowered germination in response to KAR1 and GA3. Gene expression studies preceded by the identification of several genes related to ethylene biosynthesis (AfACS6, AfACO1, and AfACO5) and perception (AfERS1b, AfERS1c, AfERS2, AfETR2, AfETR3, and AfETR4) provided further evidence for the engagement of ethylene in KAR1 and GA3 induced germination of A. fatua caryopses. Both AfACO1 and AfACO5 were upregulated, whereas AfACS6 remained unaffected by the treatment. This suggests the existence of different regulatory mechanisms of enzymatic activity, transcriptional for ACO and non-transcriptional for ACS. During imbibition in water, AfERS1b was stronger expressed than other receptor genes. In the presence of KAR1 or GA3, the expression of AfETR3 was substantially induced. Differential expression of ethylene receptor genes implies the modulation of caryopses sensitivity adjusted to ethylene availability and suggests the functional diversification of individual receptors.