Phenotypic diversity and biochemical characteristics of selected rhizobia nodulating the common bean (Phaseolus vulgaris L.)

Increasing interest in using rhizobia as biofertilizers in smallholder agricultural farming systems has prompted scientists to investigate rhizobia diversity, resulting in the identification of many strains. Fifty-five Rhizobium strains nodulating in the common bean (Phaseolus vulgaris L.) were isolated from soil samples from different areas of Tunisia and phenotypically characterized to determine their symbiotic nitrogen fixation capabilities. Their tolerance to pH, salinity, temperature and alkalinity, as well as their cultural and biochemical characteristics indicated wide physiological diversity. These phenotypic characteristics significantly affected rhizobia growth, and strains of interest were identified and used in inoculation trials. They were efficient and able to tolerate pH from 4 to 9, NaCl concentrations of 25 to 100 mM, temperature variation from 10 to 40 °C, and lime (CaCO3) from 0.05 to 0.20 mM. Selected Rhizobium strains were identified as candidates for biofertilizer production for a variety of Tunisian soil types.

90% Accuracy for Photoplethysmography-Based Non-Invasive Blood Glucose Prediction by Deep Learning with Cohort Arrangement and Quarterly Measured HbA1c

Previously published photoplethysmography-(PPG) based non-invasive blood glucose (NIBG) measurements have not yet been validated over 500 subjects. As illustrated in this work, we increased the number subjects recruited to 2538 and found that the prediction accuracy (the ratio in zone A of Clarke’s error grid) reduced to undesirable 60.6%. We suspect the low prediction accuracy induced by larger sample size might arise from the physiological diversity of subjects, and one possibility is that the diversity might originate from medication. Therefore, we split the subjects into two cohorts for deep learning: with and without medication (1682 and 856 recruited subjects, respectively). In comparison, the cohort training for subjects without any medication had approximately 30% higher prediction accuracy over the cohort training for those with medication. Furthermore, by adding quarterly (every 3 months) measured glycohemoglobin (HbA1c), we were able to significantly boost the prediction accuracy by approximately 10%. For subjects without medication, the best performing model with quarterly measured HbA1c achieved 94.3% prediction accuracy, RMSE of 12.4 mg/dL, MAE of 8.9 mg/dL, and MAPE of 0.08, which demonstrates a very promising solution for NIBG prediction via deep learning. Regarding subjects with medication, a personalized model could be a viable means of further investigation.

Key Cannabis Salt-Responsive Genes and Pathways Revealed by Comparative Transcriptome and Physiological Analyses of Contrasting Varieties

For the dissection and identification of the molecular response mechanisms to salt stress in cannabis, an experiment was conducted surveying the diversity of physiological characteristics. RNA-seq profiling was carried out to identify differential expression genes and pathway which respond to salt stress in different cannabis materials. The result of physiological diversity analyses showed that it is more sensitive to proline contents in K94 than in W20; 6 h was needed to reach the maximum in K94, compared to 12 h in W20. For profiling 0–72 h after treatment, a total of 10,149 differentially expressed genes were identified, and 249 genes exhibited significantly diverse expression levels in K94, which were clustered in plant hormone signal transduction and the MAPK signaling pathway. A total of 371 genes showed significant diversity expression variations in W20, which were clustered in the phenylpropanoid biosynthesis and plant hormone signal transduction pathway. The pathway enrichment by genes which were identified in K94 and W20 showed a similar trend to those clustered in plant hormone signal transduction pathways and MAPK signaling. Otherwise, there were 85 genes which identified overlaps between the two materials, indicating that these may be underlying genes related to salt stress in cannabis. The 86.67% agreement of the RNA-seq and qRT-PCR indicated the accuracy and reliability of the RNA-seq technique. Additionally, the result of physiological diversity was consistent with the predicted RNA-seq-based findings. This research may offer new insights into the molecular networks mediating cannabis to respond to salt stress.

Interspecific variation in evaporative water loss and temperature response, but not metabolic rate, among hibernating bats

Hibernation is widespread among mammals in a variety of environmental contexts. However, few experimental studies consider interspecific comparisons, which may provide insight into general patterns of hibernation strategies. We studied 13 species of free-living bats, including populations spread over thousands of kilometers and diverse habitats. We measured torpid metabolic rate (TMR) and evaporative water loss (two key parameters for understanding hibernation energetics) across a range of temperatures. There was no difference in minimum TMR among species (i.e., all species achieved similarly low torpid metabolic rate) but the temperature associated with minimum TMR varied among species. The minimum defended temperature (temperature below which TMR increased) varied from 8 °C to < 2 °C among species. Conversely, evaporative water loss varied among species, with species clustered in two groups representing high and low evaporative water loss. Notably, species that have suffered population declines due to white-nose syndrome fall in the high evaporative water loss group and less affected species in the low evaporative water loss group. Documenting general patterns of physiological diversity, and associated ecological implications, contributes to broader understanding of biodiversity, and may help predict which species are at greater risk of environmental and anthropogenic stressors.

Morpho-Physiological Diversity and Its Association with Herbicide Resistance in Echinochloa Ecotypes

The genus Echinochloa constitutes some of the most prominent weed species found in rice (Oryza sativa L.) production worldwide. The taxonomy of Echinochloa is complex due to its morphological variations. The morpho-physiological diversity and taxonomic characteristics of Echinochloa ecotypes infesting rice fields in Texas are unknown. A total of 54 Echinochloa ecotypes collected during late-season field surveys in 2015 and 2016 were characterized in a common garden in 2017. Plants were characterized for 14 morpho-physiological traits including stem angle; stem color; plant height; leaf color; leaf texture; flag leaf length, width, and angle; days to flowering; panicle length; plant biomass; seed shattering; seed yield; and seed dormancy. Principal component analysis indicated that four (plant height, flag leaf length, seed shattering, and seed germination) of the 14 phenological traits characterized here had significantly contributed to the overall morphological diversity of Echinochloa spp. Results showed wide inter-population diversity for the measured traits among the E. colona ecotypes, as well as diverse intra-population variability in all three Echinochola species studied, including barnyardgrass [Echinochloa crus-galli (L.) P. Beauv.], junglerice [Echinochloa colona (L.) Link], and rough barnyardgrass [Echinochloa muricata (P. Beauv.) Fernald]. Taxonomical classification revealed that the collection consisted of three Echinochloa species, with E. colona being the most dominant (96%), followed by E. crus-galli (2%), and E. muricata (2%). Correlation analysis of morpho-physiological traits and resistance status to commonly used preemergence (clomazone, quinclorac) and postemergence herbicides (propanil, quinclorac, imazethapyr, and fenoxaprop-ethyl) failed to show any significant association. Findings from this study provided novel insights into the morpho-physiological characteristics of Echinochloa ecotypes in rice production in Texas. The morphological diversity currently present in Echinochloa ecotypes could contribute to their adaptation to selection pressure imposed by different management tools, emphasizing the need for a diversified management approach to effectively control this weed species.

Genetic and physiological diversity of white Spanish broom (Chamaecytisus albus) endophytes

Chamaecytisus albus (Spanish broom) is a legume shrub that can be found in only one natural locality in Poland. This specimen is critically endangered; therefore, different actions focusing on protection of this plant in the natural habitat are undertaken, and one of them involves studies of the population of Chamaecytisus albus bacterial endophytes, which in the future could be used as bioprotectants and/or biofertilizers. A collection of 94 isolates was obtained from Spanish broom nodules, and the physiological and genetic diversity of these strains was studied. A few potentially beneficial traits were detected, i.e. secretion of cellulases (66 isolates), production of siderophores (60 isolates), phosphate solubilization (25 isolates), and production of IAA (58 isolates), indole (16 isolates), or HCN (3 isolates). Twenty-nine of the 94 tested isolates were able to induce the development of root nodules in plants grown in vitro and can therefore be assumed as Chamaecytisus albus symbionts. Genome fingerprinting by BOX-PCR, as well as gyrB and nodZ gene sequencing revealed a great genetic diversity of specimens in the collection. The symbiotic isolates were classified in different clades, suggesting they could belong to different species, however, most of them revealed sequence similarity to Bradyrhizobium genus.

Deep learning based kcat prediction enables improved enzyme constrained model reconstruction

Enzyme turnover numbers (kcat values) are key parameters to understand cell metabolism, proteome allocation and physiological diversity, but experimentally measured kcat data are sparse and noisy. Here we provide a deep learning approach to predict kcat values for metabolic enzymes in a high-throughput manner with the input of substrate structures and protein sequences. Our approach can capture kcat changes for mutated enzymes and identify amino acid residues with great impact on kcat values. Furthermore, we applied the approach to predict genome scale kcat values for over 300 yeast species, demonstrating that the predicted kcat values are consistent with current evolutional understanding. Additionally, we designed an automatic pipeline using the predicted kcat values to parameterize enzyme-constrained genome scale metabolic models (ecGEMs) facilitated by a Bayesian approach, which outperformed the default ecGEMs in predicting phenotypes and proteomes and enabled to explain phenotype differences among yeast species. The deep learning kcat prediction approach and automatic ecGEM construction pipeline would thus be a valuable tool to uncover the global trend of enzyme kinetics and physiological diversity, and to further elucidate cell metabolism on a large scale.

Life in the Dark: Phylogenetic and Physiological Diversity of Chemosynthetic Symbioses

Possibly the last discovery of a previously unknown major ecosystem on Earth was made just over half a century ago, when researchers found teaming communities of animals flourishing two and a half kilometers below the ocean surface at hydrothermal vents. We now know that these highly productive ecosystems are based on nutritional symbioses between chemosynthetic bacteria and eukaryotes and that these chemosymbioses are ubiquitous in both deep-sea and shallow-water environments. The symbionts are primary producers that gain energy from the oxidation of reduced compounds, such as sulfide and methane, to fix carbon dioxide or methane into biomass to feed their hosts. This review outlines how the symbiotic partners have adapted to living together. We first focus on the phylogenetic and metabolic diversity of these symbioses and then highlight selected research directions that could advance our understanding of the processes that shaped the evolutionary and ecological success of these associations. Expected final online publication date for the Annual Review of Microbiology, Volume 75 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Phylogenomic disentangling of the Bifidobacterium longum subsp. infantis taxon

Members of the Bifidobacterium longum species have been shown to possess adaptive abilities to allow colonization of different mammalian hosts, including humans, primates and domesticated mammalian species, such as dogs, horses, cattle and pigs. To date, three subspecies have formally been recognized to belong to this bifidobacterial taxon, i.e. B. longum subsp. longum , B. longum subsp. infantis and B. longum subsp. suis . Although B. longum subsp. longum is widely distributed in the human gut irrespective of host age, B. longum subsp. infantis appears to play a significant role as a prominent member of the gut microbiota of breast-fed infants. Nevertheless, despite the considerable scientific relevance of these taxa and the vast body of genomic data now available, an accurate dissection of the genetic features that comprehensively characterize the B. longum species and its subspecies is still missing. In the current study, we employed 261 publicly available B. longum genome sequences, combined with those of 11 new isolates, to investigate genomic diversity of this taxon through comparative genomic and phylogenomic approaches. These analyses allowed us to highlight a remarkable intra-species genetic and physiological diversity. Notably, characterization of the genome content of members of B. longum subsp. infantis subspecies suggested that this taxon may have acquired genetic features for increased competitiveness in the gut environment of suckling hosts. Furthermore, specific B. longum subsp. infantis genomic features appear to be responsible for enhanced horizontal gene transfer (HGT) occurrences, underpinning an intriguing dedication toward acquisition of foreign DNA by HGT events.

Freeze tolerance in neotropical frogs: an intrageneric comparison using Pristimantis species of high elevation and medium elevation

Paramos are high-elevation tropical Andean ecosystems above the tree line that display variable temperature and frequent freezing spells. Because a significant anuran community lives in this environment, physiological protection against freezing must characterise individuals in this community. Antifreeze protection has been studied in amphibians from other communities, and it is likely that Paramo anurans rely on the same underlying molecules that convey such protection to Nearctic species. However, given the pervasive presence of freezing spells in the Paramos year-round, the processes of activating protection mechanisms may differ from that of seasonal counterparts. Accordingly, this study investigated cryoprotection strategies in high-elevation tropical frogs, using as a model the terrestrial and nocturnal genus Pristimantis, specifically P. bogotensis, P. elegans and P. nervicus from Paramos, and the warm ecosystem counterparts P. insignitus, P. megalops and P. sanctaemartae. We focused on freeze tolerance and its relationship with glucose accumulation and ice formation. Under field conditions, the highest elevation P. nervicus exhibited higher glucose concentration at dawn compared to noon (1.7 ± 0.6 mmol/L versus 3.5 ± 1.32 mmol/L). Under experimental thermal freeze exposure for 2 hours between −2 and −4 ºC, the glucose concentration of the three Paramo species increased but physiological diversity was evident (P. nervicus 126%; P. bogotensis 100%; and P. elegans 55%). During this test, body ice formation was assessed calorimetrically. The species with the highest body ice formation was P. bogotensis (17% ± 5.37; maximum value: 63%; n = 8), followed by P. nervicus (5% ± 3.27; maximum value: 11%; n = 5) and P. elegans (0.34% ± 0.09; maximum value: 1%; n = 4). The study shows physiological diversity both within a genus and across the amphibian community around the freezing contour. Overall, Paramo species differ in freezing physiology from their low-elevation counterparts. Thus, climate shifts increasing freezing spells may affect the structure of communities in this zone.