Background: Regulatory factor X (RFX) transcription factors play a key role in ciliary assembly in nematode, Drosophila and mouse. Using the tremendous advantages of comparative genomics in closely related species, we identified novel genes regulated by dRFX in Drosophila. Results: We first demonstrate that a subset of known ciliary genes in Caenorhabditis elegans and Drosophila are regulated by dRFX and have a conserved RFX binding site (X-box) in their promoters in two highly divergent Drosophila species. We then designed an X-box consensus sequence and carried out a genome wide computer screen to identify novel genes under RFX control. We found 412 genes that share a conserved X-box upstream of the ATG in both species, with 83 genes presenting a more restricted consensus. We analyzed 25 of these 83 genes, 16 of which are indeed RFX target genes. Two of them have never been described as involved in ciliogenesis. In addition, reporter construct expression analysis revealed that three of the identified genes encode proteins specifically localized in ciliated endings of Drosophila sensory neurons. Conclusion: Our X-box search strategy led to the identification of novel RFX target genes in Drosophila that are involved in sensory ciliogenesis. We also established a highly valuable Drosophila cilia and basal body dataset. These results demonstrate the accuracy of the X-box screen and will be useful for the identification of candidate genes for human ciliopathies, as several human homologs of RFX target genes are known to be involved in diseases, such as Bardet-Biedl syndrome.

Summary: Synthesis and accumulation of the storage lipid triacylglycerol in vegetative plant tissues has emerged as a promising strategy to meet the world's future need for vegetable oil. Sorghum (Sorghum bicolor) is a particularly attractive target crop given its high biomass, drought resistance and C₄ photosynthesis. While oilseed‐like triacylglycerol levels have been engineered in the C₃ model plant tobacco, progress in C₄ monocot crops has been lagging behind. In this study, we report the accumulation of triacylglycerol in sorghum leaf tissues to levels between 3 and 8.4% on a dry weight basis depending on leaf and plant developmental stage. This was achieved by the combined overexpression of genes encoding the Zea mays WRI1 transcription factor, Umbelopsis ramanniana UrDGAT2a acyltransferase and Sesamum indicum Oleosin‐L oil body protein. Increased oil content was visible as lipid droplets, primarily in the leaf mesophyll cells. A comparison between a constitutive and mesophyll‐specific promoter driving WRI1 expression revealed distinct changes in the overall leaf lipidome as well as transitory starch and soluble sugar levels. Metabolome profiling uncovered changes in the abundance of various amino acids and dicarboxylic acids. The results presented here are a first step forward towards the development of sorghum as a dedicated biomass oil crop and provide a basis for further combinatorial metabolic engineering.

The industrial production and use of nitrogenous fertilizer involves significant environmental and economic costs. Strategies to reduce fertilizer dependency are required to address the world’s increasing demand for sustainable food, fibres and biofuels. Biological nitrogen fixation, a process unique to diazatrophic bacteria, is catalysed by the nitrogenase complex, and reconstituting this function in plant cells is an ambitious biotechnological strategy to reduce fertilizer use. Here we establish that the full array (n=16) of biosynthetic and catalytic nitrogenase (Nif) proteins from the diazotroph Klebsiella pneumoniae can be individually expressed as mitochondrial targeting peptide (MTP)-Nif fusions in Nicotiana benthamiana. We show that these are correctly targeted to the plant mitochondrial matrix, a subcellular location with biochemical and genetic characteristics potentially supportive of nitrogenase function. Although Nif proteins B,D,E,F,H,J,K,M,N,Q,S,U,V,X,Y and Z were all detectable by Western blot analysis, the NifD catalytic component was the least abundant. To address this problem, a translational fusion between NifD and NifK was designed based on the crystal structure of the nitrogenase MoFe protein heterodimer. This fusion protein enabled equimolar NifD:NifK stoichiometry and improved NifD expression levels in plants. Finally, four MTP-Nif fusion proteins (B,S,H,Y) were successfully expressed, demonstrating that multiple components of nitrogenase can targeted to plant mitochondria. These results establish the feasibility of reconstituting the complete componentry for nitrogenase in plant cells, within an intracellular environment that could support the conversion of nitrogen gas into ammonia.

Topological deep learning (TDL) is an emerging area that combines the principles of Topological data analysis (TDA) with deep learning techniques. TDA provides insight into data shape; it obtains global descriptions of multi-dimensional data whilst exhibiting robustness to deformation and noise. Such properties are desirable in deep learning pipelines, but they are typically obtained using non-TDA strategies. This is partly caused by the difficulty of combining TDA constructs (e.g. barcode and persistence diagrams) with current deep learning algorithms. Fortunately, we are now witnessing a growth of deep learning applications embracing topologically-guided components. In this survey, we review the nascent field of topological deep learning by first revisiting the core concepts of TDA. We then explore how the use of TDA techniques has evolved over time to support deep learning frameworks, and how they can be integrated into different aspects of deep learning. Furthermore, we touch on TDA usage for analyzing existing deep models; deep topological analytics. Finally, we discuss the challenges and future prospects of topological deep learning.