This study reports the application of a novel bioprospecting procedure designed to screen plant growth-promoting rhizobacteria (PGPR) capable of rapidly colonizing the rhizosphere and mitigating drought stress in multiple hosts. Two PGPR strains were isolated by this bioprospecting screening assay and identified as Bacillus sp. (12D6) and Enterobacter sp. (16i). When inoculated into the rhizospheres of wheat (Triticum aestivum) and maize (Zea mays) seedlings, these PGPR resulted in delays in the onset of plant drought symptoms. The plant phenotype responding to drought stress was associated with alterations in root system architecture. In wheat, both PGPR isolates significantly increased root branching, and Bacillus sp. (12D6), in particular, increased root length, when compared to the control. In maize, both PGPR isolates significantly increased root length, root surface area and number of tips when compared to the control. Enterobacter sp. (16i) exhibited greater effects in root length, diameter and branching when compared to Bacillus sp. (12D6) or the control. In vitro phytohormone profiling of PGPR pellets and filtrates using LC/MS demonstrated that both PGPR strains produced and excreted indole-3-acetic acid (IAA) and salicylic acid (SA) when compared to other phytohormones. The positive effects of PGPR inoculation occurred concurrently with the onset of water deficit, demonstrating the potential of the PGPR identified from this bioprospecting pipeline for use in crop production systems under drought stress.

Host-mediated microbiome engineering (HMME) is a strategy that utilizes the host phenotype to indirectly select microbiomes though cyclic differentiation and propagation. In this experiment, the host phenotype of delayed onset of seedling water deficit stress symptoms was used to infer beneficial microbiome-host interactions over multiple generations. By utilizing a host-centric selection approach, microbiota are selected at a community level, therein using artificial selection to alter microbiomes through both ecological and evolutionary processes. After six rounds of artificial selection using host-mediated microbiome engineering (HMME), a microbial community was selected that mediated a 5-day delay in the onset of drought symptoms in wheat seedlings. Seedlings grown in potting medium inoculated with the engineered rhizosphere from the 6th round of HMME produced significantly more biomass and root system length, dry weight, and surface area than plants grown in medium similarly mixed with autoclaved inoculum (negative control). The effect on plant water stress tolerance conferred by the inoculum was transferable at subsequent 10-fold and 100-fold dilutions in fresh non-autoclaved medium but was lost at 1000-fold dilution and was completely abolished by autoclaving, indicating the plant phenotype is mediated by microbial population dynamics. The results from 16S rRNA amplicon sequencing of the rhizosphere microbiomes at rounds 0, 3, and 6 revealed taxonomic increases in proteobacteria at the phylum level and betaproteobacteria at the class level. There were significant decreases in alpha diversity in round 6, divergence in speciation with beta diversity between round 0 and 6, and changes in overall community composition. This study demonstrates the potential of using the host as a selective marker to engineer microbiomes that mediate changes in the rhizosphere environment that improve plant adaptation to drought stress.

The COVID-19 pandemic has sparked an urgent need to uncover the underlying biology of this devastating disease. Though RNA viruses mutate more rapidly than DNA viruses, there are a relatively small number of single nucleotide polymorphisms (SNPs) that differentiate the main SARS-CoV-2 lineages that have spread throughout the world. In this study, we investigated 129 RNA-seq data sets and 6928 consensus genomes to contrast the intra-host and inter-host diversity of SARS-CoV-2. Our analyses yielded three major observations. First, the mutational profile of SARS-CoV-2 highlights intra-host single nucleotide variant (iSNV) and SNP similarity, albeit with differences in C > U changes. Second, iSNV and SNP patterns in SARS-CoV-2 are more similar to MERS-CoV than SARS-CoV-1. Third, a significant fraction of insertions and deletions contribute to the genetic diversity of SARS-CoV-2. Altogether, our findings provide insight into SARS-CoV-2 genomic diversity, inform the design of detection tests, and highlight the potential of iSNVs for tracking the transmission of SARS-CoV-2.

Nitrogen (N) is one of the most important limiting factors in conventional rice (Oryza sativa) production, which heavily relies on synthetic fertilizers. In this study, we researched on the development and use of a vertical semi-closed airlift photobioreactor (PBR) for microalgal cultivation and subsequently determined the efficacy of microalgae-based fertilizers to rice plant growth. The PBR system was developed to produce two strains of N2-fixing cyanobacteria (Anabaena sp. UTEX 2576, Nostoc muscorum UTEX 2209S), and a polyculture of Chlorella vulgaris (UTEX 2714) and Scenedesmus dimorphus (UTEX 1237). When these biofertilizers were evaluated for rice under the greenhouse conditions, results showed that the rice plant heights treated with polyculture-based microalgal biomass were similar to or better than the urea treatment. The effects of the inoculation of the N2-fixing cyanobacterial inoculation on seedling growth was not statistically significant. In conclusion, the vertical semi-closed system PBR cultivation method developed in this study proved to be a simple and effective method for cultivating microalgae. Demonstration of the reliable production system for N2-fixing cyanobacteria and chlorophytes at a medium scale could potentially open the future application of microalgal biofertilizers in rice production.

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Computational analysis of host-associated microbiomes has opened the door to numerous discoveries relevant to human health and disease. However, contaminant sequences in metagenomic samples can potentially impact the interpretation of findings reported in microbiome studies, especially in low-biomass environments. Contamination from DNA extraction kits or sampling lab environments leaves taxonomic "bread crumbs" across multiple distinct sample types. Here we describe Squeegee, a de novo contamination detection tool that is based upon this principle, allowing the detection of microbial contaminants when negative controls are unavailable. On the low-biomass samples, we compare Squeegee predictions to experimental negative control data and show that Squeegee accurately recovers putative contaminants. We analyze samples of varying biomass from the Human Microbiome Project and identify likely, previously unreported kit contamination. Collectively, our results highlight that Squeegee can identify microbial contaminants with high precision and thus represents a computational approach for contaminant detection when negative controls are unavailable.

In October 2020, 62 scientists from nine nations worked together remotely in the Second Baylor College of Medicine & DNAnexus hackathon, focusing on different related topics on Structural Variation, Pan-genomes, and SARS-CoV-2 related research.   The overarching focus was to assess the current status of the field and identify the remaining challenges. Furthermore, how to combine the strengths of the different interests to drive research and method development forward. Over the four days, eight groups each designed and developed new open-source methods to improve the identification and analysis of variations among species, including humans and SARS-CoV-2. These included improvements in SV calling, genotyping, annotations and filtering. Together with advancements in benchmarking existing methods. Furthermore, groups focused on the diversity of SARS-CoV-2. Daily discussion summary and methods are available publicly at https://github.com/collaborativebioinformatics provides valuable insights for both participants and the research community.

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