Molecular characterization of vermicompost-derived IAA-releasing bacterial isolates and assessment of their impact on the root improvement of banana during primary hardening.

The hardening step of micropropagation is crucial to make the in vitro raised plants mature and further enhancing their survivability in the external environment. Auxin regulates various root physiological parameters in plant systems. Therefore, the present study aimed to assess the impact of three vermicompost-derived IAA-releasing microbial strains, designated S1, S2, and S3, as biofertilizers on in vitro raised banana plantlets during primary hardening. The High-Performance Thin-Layer Chromatography (HPTLC) analysis of these strains revealed a higher IAA content for S1 and S2 than that of S3 after 144 h of incubation. In total, seven different treatments were applied to banana plantlets, and significant variations were observed in all plant growth parameters for all treatments except autoclaved cocopeat (100%) mixed with autoclaved vermicompost (100%) at a 1:1 ratio. Among these treatments, the application of S3 biofertilizer: autoclaved cocopeat (1:1), followed by S2 biofertlizer: autoclaved cocopeat (1:1), was found to be better than other treatments for root numbers per plant, root length per plant, root volume, and chlorophyll content. These findings have confirmed the beneficial effects of microbial strains on plant systems and propose a link between root improvement and bacterial auxin. Further, these strains were identified at the molecular level as Bacillus sp. As per our knowledge, this is the first report of Bacillus strains isolated from vermicompost and applied as biofertilizer along with cocopeat for the primary hardening of banana. This unique approach may be adopted to improve the quality of plants during hardening, which increases their survival under abiotic stresses.

Microbiological properties of Beejamrit, an ancient Indian traditional knowledge, uncover a dynamic plant beneficial microbial network.

Beejamrit is an ancient organic formulation commonly used as a seed treatment in organic and natural farming in India. This low-cost formulation is primarily a product of dairy excreta (e.g., cow dung and cow urine) and forest soil, often supplemented with limestone. Growing data suggest that dairy excreta are the potential sources of enriched microbial niche, including several plant growth-promoting bacteria capable of synthesizing plant growth regulators. However, the microbiological properties of Beejamrit and their temporal changes after different incubation periods, delineating its application in seed treatment, remain largely unexplored. Here, we aimed to analyze the decomposition rate of Beejamrit over 7-consecutive days of incubation. This study further elucidates the microbial niche and their dynamics in Beejamrit, including the plant beneficial bacteria. We have shown that the population of plant beneficial bacteria, such as the free-living nitrogen fixers (FNFs) and the phosphate solubilizers (PSBs), proliferates progressively up to 4- and 5-days of incubation, respectively (p < 0.0001). This study also reports the total indolic content of Beejamrit, including indole 3-acetic acid (IAA), which further tends to oscillate in concentration based on the incubation periods incurred during the Beejamrit preparation. Our analyses, together, establish that Beejamrit provides a dynamic, microbe-based metabolic network and may, therefore, act as a plant biostimulant to crop plants. A plant-based bioassay finally demonstrates the role of Beejamrit in the seed treatment to improve seed germination, seedling survival rate, and shoot length trait in French beans (p < 0.01). In conclusion, this study highlights, for the first time, the scientific insights of Beejamrit as a potential seed priming agent in agriculture.

Elimination and molecular identification of endophytic bacterial contaminants during in vitro propagation of Bambusa balcooa.

Bambusa balcooa is an economically important, multipurpose bamboo species, decidedly used in construction industry. Availability of natural bamboo is depleting very rapidly due to accelerated deforestation and its unrestrained use. The large number and timely supply of saplings are the need of the hour for the restoration of bamboo stands. Micropropagation, being the potent alternative for season independent rapid regeneration, is restricted in bamboo because of endophytic contamination. An in vitro attempt has been taken to overcome the endophytic contamination by using broad spectrum antibiotics as surface sterilant as well as a media component. Ampicillin sodium salt (5 mg/ml for 30 min) as a surface sterilant was found as the best treatment for high bud breaking (80%) coupled with high branching and low contamination (20%) but it was found ineffective to control the contamination during multiplication stage. Then, two endophytes were isolated and minimum inhibitory concentration was determined through antibiotic susceptibility test for successful eradication at multiplication stage. Finally, contamination free cultures were obtained when streptocycline (100 µg/ml) and gentamicin sulphate (75 µg/ml) were added into the medium. The two isolated endophytes, BB1 and BB2, were identified through 16S rDNA techniques and NCBI-BLAST algorithm with 99% sequence similarity with those of Janibacter sp. (KX423734) and Serratia marcescens strain (KX423735). To our knowledge, this is the first report for B. balcooa where antibiotics were used as surface sterilant as well as medium component, to control endophytic bacterial contaminants, followed by their identification.

Gibberellins - a multifaceted hormone in plant growth regulatory network.

Plants tend to acclimatize to unfavourable environs by integrating growth and development to environmentally activated signals. Phytohormones strongly regulate convergent developmental and stress adaptive procedures and synchronize cellular reaction to the exogenous and endogenous conditions within the adaptive signaling networks. Gibberellins (GA), a group of tetracyclic diterpenoids, being vital regulators of plant growth, are accountable for regulating several aspects of growth and development of higher plants. If the element of reproduction is considered as an absolute requisite then for a majority of the higher plants GA signaling is simply indispensable. Latest reports have revealed unique conflicting roles of GA and other phytohormones in amalgamating growth and development in plants through environmental signaling. Numerous physiological researches have detailed substantial crosstalk between GA and other hormones like abscisic acid, auxin, cytokinin, and jasmonic acid. In this review, a number of explanations and clarifications for this discrepancy are explored based on the crosstalk among GA and other phytohormones.

Bamboo: an overview on its genetic diversity and characterization.

Genetic diversity represents the heritable variation both within and among populations of organisms, and in the context of this paper, among bamboo species. Bamboo is an economically important member of the grass family Poaceae, under the subfamily Bambusoideae. India has the second largest bamboo reserve in Asia after China. It is commonly known as "poor man's timber", keeping in mind the variety of its end use from cradle to coffin. There is a wide genetic diversity of bamboo around the globe and this pool of genetic variation serves as the base for selection as well as for plant improvement. Thus, the identification, characterization and documentation of genetic diversity of bamboo are essential for this purpose. During recent years, multiple endeavors have been undertaken for characterization of bamboo species with the aid of molecular markers for sustainable utilization of genetic diversity, its conservation and future studies. Genetic diversity assessments among the identified bamboo species, carried out based on the DNA fingerprinting profiles, either independently or in combination with morphological traits by several researchers, are documented in the present review. This review will pave the way to prepare the database of prevalent bamboo species based on their molecular characterization.

Screening of rice landraces for salinity tolerance at seedling stage through morphological and molecular markers.

The present investigation was carried out to evaluate 33 rice landrace genotypes for assessment of their salt tolerance at seedling stage. Growth parameters like root length, shoot length and plant biomass were measured after 12 days of exposure to six different levels of saline solution (with electrical conductivity of 4, 6, 8, 10, 12 or 14 dS m (-1)). Genotypes showing significant interaction and differential response towards salinity were assessed at molecular level using 11 simple sequence repeats (SSR) markers, linked with salt tolerance quantitative trait loci. Shoot length, root length and plant biomass at seedling stage decreased with increasing salinity. However, relative salt tolerance in terms of these three parameters varied among genotypes. Out of the 11 SSR markers RM8094, RM336 and RM8046, the most competent descriptors to screen the salt tolerant genotypes with higher polymorphic information content coupled with higher marker index value, significantly distinguished the salt tolerant genotypes. Combining morphological and molecular assessment, four lanraces viz. Gheus, Ghunsi, Kuthiahara and Sholerpona were considered as true salt tolerant genotypes which may contribute in greater way in the development of salt tolerant genotypes in rice.

Analysis of petroleum contaminated soils by spectral modeling and pure response profile recovery of n-hexane.

This pilot study compared penalized spline regression (PSR) and random forest (RF) regression using visible and near-infrared diffuse reflectance spectroscopy (VisNIR DRS) derived spectra of 164 petroleum contaminated soils after two different spectral pretreatments [first derivative (FD) and standard normal variate (SNV) followed by detrending] for rapid quantification of soil petroleum contamination. Additionally, a new analytical approach was proposed for the recovery of the pure spectral and concentration profiles of n-hexane present in the unresolved mixture of petroleum contaminated soils using multivariate curve resolution alternating least squares (MCR-ALS). The PSR model using FD spectra (r(2) = 0.87, RMSE = 0.580 log10 mg kg(-1), and residual prediction deviation = 2.78) outperformed all other models tested. Quantitative results obtained by MCR-ALS for n-hexane in presence of interferences (r(2) = 0.65 and RMSE 0.261 log10 mg kg(-1)) were comparable to those obtained using FD (PSR) model. Furthermore, MCR ALS was able to recover pure spectra of n-hexane.

Rapid estimation of compost enzymatic activity by spectral analysis method combined with machine learning.

The aim of this study was to investigate the feasibility of using visible near-infrared (VisNIR) diffuse reflectance spectroscopy (DRS) as an easy, inexpensive, and rapid method to predict compost enzymatic activity, which traditionally measured by fluorescein diacetate hydrolysis (FDA-HR) assay. Compost samples representative of five different compost facilities were scanned by DRS, and the raw reflectance spectra were preprocessed using seven spectral transformations for predicting compost FDA-HR with six multivariate algorithms. Although principal component analysis for all spectral pretreatments satisfactorily identified the clusters by compost types, it could not separate different FDA contents. Furthermore, the artificial neural network multilayer perceptron (residual prediction deviation=3.2, validation r(2)=0.91 and RMSE=13.38 µg g(-1) h(-1)) outperformed other multivariate models to capture the highly non-linear relationships between compost enzymatic activity and VisNIR reflectance spectra after Savitzky-Golay first derivative pretreatment. This work demonstrates the efficiency of VisNIR DRS for predicting compost enzymatic as well as microbial activity.

Spectral data mining for rapid measurement of organic matter in unsieved moist compost.

Fifty-five compost samples were collected and scanned as received by visible and near-IR (VisNIR, 350-2500 nm) diffuse reflectance spectroscopy. The raw reflectance and first-derivative spectra were used to predict log(10)-transformed organic matter (OM) using partial least squares (PLS) regression, penalized spline regression (PSR), and boosted regression trees (BRTs). Incorporating compost pH, moisture percentage, and electrical conductivity as auxiliary predictors along with reflectance, both PLS and PSR models showed comparable cross-validation r(2) and validation root-mean-square deviation (RMSD). The BRT-reflectance model exhibited best predictability (residual prediction deviation=1.61, cross-validation r(2)=0.65, and RMSD=0.09 log(10)%). These results proved that the VisNIR-BRT model, along with easy-to-measure auxiliary variables, has the potential to quantify compost OM with reasonable accuracy.