Machine Learning-Based Classification of Lignocellulosic Biomass from Pyrolysis-Molecular Beam Mass Spectrometry Data.

High-throughput analysis of biomass is necessary to ensure consistent and uniform feedstocks for agricultural and bioenergy applications and is needed to inform genomics and systems biology models. Pyrolysis followed by mass spectrometry such as molecular beam mass spectrometry (py-MBMS) analyses are becoming increasingly popular for the rapid analysis of biomass cell wall composition and typically require the use of different data analysis tools depending on the need and application. Here, the authors report the py-MBMS analysis of several types of lignocellulosic biomass to gain an understanding of spectral patterns and variation with associated biomass composition and use machine learning approaches to classify, differentiate, and predict biomass types on the basis of py-MBMS spectra. Py-MBMS spectra were also corrected for instrumental variance using generalized linear modeling (GLM) based on the use of select ions relative abundances as spike-in controls. Machine learning classification algorithms e.g., random forest, k-nearest neighbor, decision tree, Gaussian Naïve Bayes, gradient boosting, and multilayer perceptron classifiers were used. The k-nearest neighbors (k-NN) classifier generally performed the best for classifications using raw spectral data, and the decision tree classifier performed the worst. After normalization of spectra to account for instrumental variance, all the classifiers had comparable and generally acceptable performance for predicting the biomass types, although the k-NN and decision tree classifiers were not as accurate for prediction of specific sample types. Gaussian Naïve Bayes (GNB) and extreme gradient boosting (XGB) classifiers performed better than the k-NN and the decision tree classifiers for the prediction of biomass mixtures. The data analysis workflow reported here could be applied and extended for comparison of biomass samples of varying types, species, phenotypes, and/or genotypes or subjected to different treatments, environments, etc. to further elucidate the sources of spectral variance, patterns, and to infer compositional information based on spectral analysis, particularly for analysis of data without a priori knowledge of the feedstock composition or identity.

Ferredoxin5 Deletion Affects Metabolism of Algae during the Different Phases of Sulfur Deprivation.

Ferredoxin5 (FDX5), a minor ferredoxin protein in the alga Chlamydomonas (Chlamydomonas reinhardtii), helps maintain thylakoid membrane integrity in the dark. Sulfur (S) deprivation has been used to achieve prolonged hydrogen production in green algae. Here, we propose that FDX5 is involved in algal responses to S-deprivation as well as to the dark. Specifically, we tested the role of FDX5 in both the initial aerobic and subsequent anaerobic phases of S-deprivation. Under S-deprived conditions, absence of FDX5 causes a distinct delay in achieving anoxia by affecting photosynthetic O2 evolution, accompanied by reduced acetate uptake, lower starch accumulation, and delayed/lower fermentative metabolite production, including photohydrogen. We attribute these differences to transcriptional and/or posttranslational regulation of acetyl-CoA synthetase and ADP-Glc pyrophosphorylase, and increased stability of the PSII D1 protein. Interestingly, increased levels of FDX2 and FDX1 were observed in the mutant under oxic, S-replete conditions, strengthening our previously proposed hypothesis that other ferredoxins compensate in response to a lack of FDX5. Taken together, the results of our omics and pull-down experiments confirmed biochemical and physiological results, suggesting that FDX5 may have other effects on Chlamydomonas metabolism through its interaction with multiple redox partners.

Improved knockdown from artificial microRNAs in an enhanced miR-155 backbone: a designer's guide to potent multi-target RNAi.

Artificial microRNA (amiRNA) sequences embedded in natural microRNA (miRNA) backbones have proven to be useful tools for RNA interference (RNAi). amiRNAs have reduced off-target and toxic effects compared to other RNAi-based methods such as short-hairpin RNAs (shRNA). amiRNAs are often less effective for knockdown, however, compared to their shRNA counterparts. We screened a large empirically-designed amiRNA set in the synthetic inhibitory BIC/miR-155 RNA (SIBR) scaffold and show common structural and sequence-specific features associated with effective amiRNAs. We then introduced exogenous motifs into the basal stem region which increase amiRNA biogenesis and knockdown potency. We call this modified backbone the enhanced SIBR (eSIBR) scaffold. Using chained amiRNAs for multi-gene knockdown, we show that concatenation of miRNAs targeting different genes is itself sufficient for increased knockdown efficacy. Further, we show that eSIBR outperforms wild-type SIBR (wtSIBR) when amiRNAs are chained. Finally, we use a lentiviral expression system in cultured neurons, where we again find that eSIBR amiRNAs are more potent for multi-target knockdown of endogenous genes. eSIBR will be a valuable tool for RNAi approaches, especially for studies where knockdown of multiple targets is desired.

Genome-Wide Transcription Factor DNA Binding Sites and Gene Regulatory Networks in Clostridium thermocellum.

Clostridium thermocellum is a thermophilic bacterium recognized for its natural ability to effectively deconstruct cellulosic biomass. While there is a large body of studies on the genetic engineering of this bacterium and its physiology to-date, there is limited knowledge in the transcriptional regulation in this organism and thermophilic bacteria in general. The study herein is the first report of a large-scale application of DNA-affinity purification sequencing (DAP-seq) to transcription factors (TFs) from a bacterium. We applied DAP-seq to > 90 TFs in C. thermocellum and detected genome-wide binding sites for 11 of them. We then compiled and aligned DNA binding sequences from these TFs to deduce the primary DNA-binding sequence motifs for each TF. These binding motifs are further validated with electrophoretic mobility shift assay (EMSA) and are used to identify individual TFs' regulatory targets in C. thermocellum. Our results led to the discovery of novel, uncharacterized TFs as well as homologues of previously studied TFs including RexA-, LexA-, and LacI-type TFs. We then used these data to reconstruct gene regulatory networks for the 11 TFs individually, which resulted in a global network encompassing the TFs with some interconnections. As gene regulation governs and constrains how bacteria behave, our findings shed light on the roles of TFs delineated by their regulons, and potentially provides a means to enable rational, advanced genetic engineering of C. thermocellum and other organisms alike toward a desired phenotype.

Methods for Algal Protein Isolation and Proteome Analysis.

Microalgae present promising feedstocks to produce renewable fuel and chemical intermediates, in part due to high storage carbon flux capacity to triacylglycerides or storage carbohydrates upon nutrient deprivation. However, the mechanism(s) governing deprivation-mediated carbon partitioning remain to be fully elucidated, limiting targeted strain engineering strategies in algal biocatalysts. Though genomic and transcriptomic analyses offer key insights into these mechanisms, active post-transcriptional regulatory mechanisms, ubiquitous in many microalgae, necessitate proteomic and post-translational (e.g., phospho- and nitroso-proteomic) analyses to more completely evaluate algal responsiveness to nutrient deprivation. Herein, we describe methods for isolating total algal protein and conducting proteomic, phosphoproteomic, and nitrosoproteomic analyses. We focus on methods deployed for the chlorophyte, Chlorella vulgaris, a model oleaginous alga with high flux to renewable fuel and chemical precursors.

Wireworm (Coleoptera: Elateridae) genomic analysis reveals putative cryptic species, population structure, and adaptation to pest control.

The larvae of click beetles (Coleoptera: Elateridae), known as "wireworms," are agricultural pests that pose a substantial economic threat worldwide. We produced one of the first wireworm genome assemblies (Limonius californicus), and investigated population structure and phylogenetic relationships of three species (L. californicus, L. infuscatus, L. canus) across the northwest US and southwest Canada using genome-wide markers (RADseq) and genome skimming. We found two species (L. californicus and L. infuscatus) are comprised of multiple genetically distinct groups that diverged in the Pleistocene but have no known distinguishing morphological characters, and therefore could be considered cryptic species complexes. We also found within-species population structure across relatively short geographic distances. Genome scans for selection provided preliminary evidence for signatures of adaptation associated with different pesticide treatments in an agricultural field trial for L. canus. We demonstrate that genomic tools can be a strong asset in developing effective wireworm control strategies.

Development of a high-productivity, halophilic, thermotolerant microalga Picochlorum renovo.

Microalgae are promising biocatalysts for applications in sustainable fuel, food, and chemical production. Here, we describe culture collection screening, down-selection, and development of a high-productivity, halophilic, thermotolerant microalga, Picochlorum renovo. This microalga displays a rapid growth rate and high diel biomass productivity (34 g m-2 day-1), with a composition well-suited for downstream processing. P. renovo exhibits broad salinity tolerance (growth at 107.5 g L-1 salinity) and thermotolerance (growth up to 40 °C), beneficial traits for outdoor cultivation. We report complete genome sequencing and analysis, and genetic tool development suitable for expression of transgenes inserted into the nuclear or chloroplast genomes. We further evaluate mechanisms of halotolerance via comparative transcriptomics, identifying novel genes differentially regulated in response to high salinity cultivation. These findings will enable basic science inquiries into control mechanisms governing Picochlorum biology and lay the foundation for development of a microalga with industrially relevant traits as a model photobiology platform.

Analysis of microbial community variation during the mixed culture fermentation of agricultural peel wastes to produce lactic acid.

Mixed cultures fermentation can be used to convert organic wastes into various chemicals and fuels. This study examined the fermentation performance of four batch reactors fed with different agricultural (orange, banana, and potato (mechanical and steam)) peel wastes using mixed cultures, and monitored the interval variation of reactor microbial communities with 16S rRNA genes using Illumina sequencing. All four reactors produced similar chemical profile with lactic acid (LA) as dominant compound. Acetic acid and ethanol were also observed with small fractions. The Illumina sequencing results revealed the diversity of microbial community decreased during fermentation and a community of largely lactic acid producing bacteria dominated by species of Lactobacillus developed.

Mitochondrial Genome Sequence of the Galápagos Endemic Land Snail Naesiotus nux.

We report herein the draft mitochondrial genome sequence of Naesiotus nux, a Galápagos endemic land snail species of the genus Naesiotus. The circular genome is 15 kb and encodes 13 protein-coding genes, 2 rRNA genes, and 21 tRNA genes.

Full Mitochondrial Genome Sequence of the Sugar Beet Wireworm Limonius californicus (Coleoptera: Elateridae), a Common Agricultural Pest.

We report here the full mitochondrial genome sequence of Limonius californicus, a species of click beetle that is an agricultural pest in its larval form. The circular genome is 16.5 kb and contains 13 protein-coding genes, 2 rRNA genes, and 22 tRNA genes.