Neural circuits carry out complex computations that allow animals to evaluate food, select mates, move toward attractive stimuli, and move away from threats. In insects, the subesophageal zone (SEZ) is a brain region that receives gustatory, pheromonal, and mechanosensory inputs and contributes to the control of diverse behaviors, including feeding, grooming, and locomotion. Despite its importance in sensorimotor transformations, the study of SEZ circuits has been hindered by limited knowledge of the underlying diversity of SEZ neurons. Here, we generate a collection of split-GAL4 lines that provides precise genetic targeting of 138 different SEZ cell types in adult , comprising approximately one third of all SEZ neurons. We characterize the single cell anatomy of these neurons and find that they cluster by morphology into six supergroups that organize the SEZ into discrete anatomical domains. We find that the majority of local SEZ interneurons are not classically polarized, suggesting rich local processing, whereas SEZ projection neurons tend to be classically polarized, conveying information to a limited number of higher brain regions. This study provides insight into the anatomical organization of the SEZ and generates resources that will facilitate further study of SEZ neurons and their contributions to sensory processing and behavior.

Detecting meaningful structure in neural activity and connectivity data is challenging in the presence of hidden nonlinearities, where traditional eigenvalue-based methods may be misleading. We introduce a novel approach to matrix analysis, called clique topology, that extracts features of the data invariant under nonlinear monotone transformations. These features can be used to detect both random and geometric structure, and depend only on the relative ordering of matrix entries. We then analyzed the activity of pyramidal neurons in rat hippocampus, recorded while the animal was exploring a 2D environment, and confirmed that our method is able to detect geometric organization using only the intrinsic pattern of neural correlations. Remarkably, we found similar results during nonspatial behaviors such as wheel running and rapid eye movement (REM) sleep. This suggests that the geometric structure of correlations is shaped by the underlying hippocampal circuits and is not merely a consequence of position coding. We propose that clique topology is a powerful new tool for matrix analysis in biological settings, where the relationship of observed quantities to more meaningful variables is often nonlinear and unknown.

BACKGROUND: The insect brain can be divided into neuropils that are formed by neurites of both local and remote origin. The complexity of the interconnections obscures how these neuropils are established and interconnected through development. The Drosophila central brain develops from a fixed number of neuroblasts (NBs) that deposit neurons in regional clusters. RESULTS: By determining individual NB clones and pursuing their projections into specific neuropils, we unravel the regional development of the brain neural network. Exhaustive clonal analysis revealed 95 stereotyped neuronal lineages with characteristic cell-body locations and neurite trajectories. Most clones show complex projection patterns, but despite the complexity, neighboring clones often coinnervate the same local neuropil or neuropils and further target a restricted set of distant neuropils. CONCLUSIONS: These observations argue for regional clonal development of both neuropils and neuropil connectivity throughout the Drosophila central brain.

Advances in single-cell RNA-sequencing technology have resulted in a wealth of studies aiming to identify transcriptomic cell types in various biological systems. There are multiple experimental approaches to isolate and profile single cells, which provide different levels of cellular and tissue coverage. In addition, multiple computational strategies have been proposed to identify putative cell types from single-cell data. From a data generation perspective, recent single-cell studies can be classified into two groups: those that distribute reads shallowly over large numbers of cells and those that distribute reads more deeply over a smaller cell population. Although there are advantages to both approaches in terms of cellular and tissue coverage, it is unclear whether different computational cell type identification methods are better suited to one or the other experimental paradigm. This study reviews three cell type clustering algorithms, each representing one of three broad approaches, and finds that PCA-based algorithms appear most suited to low read depth data sets, whereas gene clustering-based and biclustering algorithms perform better on high read depth data sets. In addition, highly related cell classes are better distinguished by higher-depth data, given the same total number of reads; however, simultaneous discovery of distinct and similar types is better served by lower-depth, higher cell number data. Overall, this study suggests that the depth of profiling should be determined by initial assumptions about the diversity of cells in the population, and that the selection of clustering algorithm(s) is subsequently based on the depth of profiling will allow for better identification of putative transcriptomic cell types.

The evolution of sexual traits often involves correlated changes in morphology and behavior. For example, in Drosophila, divergent mating displays are often accompanied by divergent pigment patterns. To better understand how such traits co-evolve, we investigated the genetic basis of correlated divergence in wing pigmentation and mating display between the sibling species Drosophila elegans and D. gunungcola. Drosophila elegans males have an area of black pigment on their wings known as a wing spot and appear to display this spot to females by extending their wings laterally during courtship. By contrast, D. gunungcola lost both of these traits. Using Multiplexed Shotgun Genotyping (MSG), we identified a ∼440 kb region on the X chromosome that behaves like a genetic switch controlling the presence or absence of male-specific wing spots. This region includes the candidate gene optomotor-blind (omb), which plays a critical role in patterning the Drosophila wing. The genetic basis of divergent wing display is more complex, with at least two loci on the X chromosome and two loci on autosomes contributing to its evolution. Introgressing the X-linked region affecting wing spot development from D. gunungcola into D. elegans reduced pigmentation in the wing spots but did not affect the wing display, indicating that these are genetically separable traits. Consistent with this observation, broader sampling of wild D. gunungcola populations confirmed the wing spot and wing display are evolving independently: some D. gunungcola males performed wing displays similar to D. elegans despite lacking wing spots. These data suggest that correlated selection pressures rather than physical linkage or pleiotropy are responsible for the coevolution of these morphological and behavioral traits. They also suggest that the change in morphology evolved prior to the change in behavior. This article is protected by copyright. All rights reserved.

Many genomes contain rapidly evolving and highly divergent genes whose homology to genes of known function often cannot be determined from sequence similarity alone. However, coding sequence-independent features of genes, such as intron-exon boundaries, often evolve more slowly than coding sequences and can provide complementary evidence for homology. We found that a linear logistic regression classifier using only structural features of rapidly evolving bicycle aphid effector genes identified many putative bicycle homologs in aphids, phylloxerids, and scale insects, whereas sequence similarity search methods yielded few homologs in most aphids and no homologs in phylloxerids and scale insects. Subsequent examination of sequence features and intron locations supported homology assignments. Differential expression studies of newly-identified bicycle homologs, together with prior proteomic studies, support the hypothesis that BICYCLE proteins act as plant effector proteins in many aphid species and perhaps also in phylloxerids and scale insects.

The R‐specific alcohol dehydrogenase from Lactobacillus brevis (Lb‐ADH) catalyzes the enantioselective reduction of prochiral ketones to the corresponding secondary alcohols. It is stable and has broad substrate specificity. These features make this enzyme an attractive candidate for biotechnological applications. A drawback is its preference for NADP(H) as a cofactor, which is more expensive and labile than NAD(H). Structure‐based computational protein engineering was used to predict mutations to alter the cofactor specificity of Lb‐ADH. Mutations were introduced into Lb‐ADH and tested against the substrate acetophenone, with either NAD(H) or NADP(H) as cofactor. The mutant Arg38Pro showed fourfold increased activity with acetophenone and NAD(H) relative to the wild type. Both Arg38Pro and wild type exhibit a pH optimum of 5.5 with NAD(H) as cofactor, significantly more acidic than with NADP(H). These and related Lb‐ADH mutants may prove useful for the green synthesis of pharmaceutical precursors.

To ensure disjunction to opposite poles during anaphase, sister chromatids must be held together following DNA replication. This is mediated by cohesin, which is thought to entrap sister DNAs inside a tripartite ring composed of its Smc and kleisin (Scc1) subunits. How such structures are created during S phase is poorly understood, in particular whether they are derived from complexes that had entrapped DNAs prior to replication. To address this, we used selective photobleaching to determine whether cohesin associated with chromatin in G1 persists in situ after replication. We developed a non-fluorescent HaloTag ligand to discriminate the fluorescence recovery signal from labeling of newly synthesized Halo-tagged Scc1 protein (pulse-chase or pcFRAP). In cells where cohesin turnover is inactivated by deletion of WAPL, Scc1 can remain associated with chromatin throughout S phase. These findings suggest that cohesion might be generated by cohesin that is already bound to un-replicated DNA.

The contrast between the disruption of genome topology after cohesin loss and the lack of downstream gene expression changes instigates intense debates regarding the structure-function relationship between genome and gene regulation. Here, by analyzing transcriptome and chromatin accessibility at the single-cell level, we discover that, instead of dictating population-wide gene expression levels, cohesin supplies a general function to neutralize stochastic coexpression tendencies of cis-linked genes in single cells. Notably, cohesin loss induces widespread gene coactivation and chromatin co-opening tens of million bases apart in cis. Spatial genome and protein imaging reveals that cohesin prevents gene co-bursting along the chromosome and blocks spatial mixing of transcriptional hubs. Single-molecule imaging shows that cohesin confines the exploration of diverse enhancer and core promoter binding transcriptional regulators. Together, these results support that cohesin arranges nuclear topology to control gene coexpression in single cells.