Comparative neuroanatomy and genomics of hugin and pheromone biosynthesis activating neuropeptide (PBAN)

The Drosophila hugin gene encodes a prepropeptide that can potentially generate several neuropeptides.(1) The gene is expressed in 20 cells of the subesophageal ganglion (SOG) that are involved in modulating feeding behavior.(2) One of the hugin neuropeptides shares homology with mammalian neuromedin U8 (NmU8), which has been shown to regulate feeding behavior in rodents.(3,4) Recent clonal analysis indicated that each hugin expressing neuron projects to one of four main targets: the protocerebrum, the ventral nerve cord, the pharynx and the corpora cardiaca.(5) In addition all hugin neurons send short neurites to a novel region ventro-lateral to the foramen, which we suggested could be the tritocerebrum. In this short article, we discuss two specific issues brought up by these analyses. One concerns the polarity of hugin neurons. The other is an evolutionary perspective on the processing of hugin neuropeptides in light of new data from mass spectrometric and genomic analyses.     

Fragment-based discovery of hydroxy-indazole-carboxamides as novel small molecule inhibitors of Hsp90

Inhibitors of the Hsp90 molecular chaperone are showing considerable promise as potential molecular therapeutic agents for the treatment of cancer. Here we describe the identification of novel small molecular weight inhibitors of Hsp90 using a fragment based approach. Fragments were selected by docking, tested in a biochemical assay and the confirmed hits were crystallized. Information gained from X-ray structures of these fragments and other chemotypes was used to drive the fragment evolution process. Optimization of these high μM binders resulted in 3-benzylindazole derivatives with significantly improved affinity and anti-proliferative effects in different human cancer cell lines.     

Response to “Vast (but avoidable) underestimation of global biodiversity”

This Formal Comment provides clarifications on the authors’ recent estimates of global bacterial diversity and the current status of the field, and responds to a Formal Comment from John Wiens regarding their prior work.

We welcome Wiens’ efforts to estimate global animal-associated bacterial richness and thank him for highlighting points of confusion and potential caveats in our previous work on the topic [1]. We find Wiens’ ideas worthy of consideration, as most of them represent a step in the right direction, and we encourage lively scientific discourse for the advancement of knowledge. Time will ultimately reveal which estimates, and underlying assumptions, came closest to the true bacterial richness; we are excited and confident that this will happen in the near future thanks to rapidly increasing sequencing capabilities. Here, we provide some clarifications on our work, its relation to Wiens’ estimates, and the current status of the field.First, Wiens states that we excluded animal-associated bacterial species in our global estimates. However, thousands of animal-associated samples were included in our analysis, and this was clearly stated in our main text (second paragraph on page 3).Second, Wiens’ commentary focuses on “S1 Text” of our paper [1], which was rather peripheral, and, hence, in the Supporting information. S1 Text [1] critically evaluated the rationale underlying previous estimates of global bacterial operational taxonomic unit (OTU) richness by Larsen and colleagues [2], but the results of S1 Text [1] did not in any way flow into the analyses presented in our main article. Indeed, our estimates of global bacterial (and archaeal) richness, discussed in our main article, are based on 7 alternative well-established estimation methods founded on concrete statistical models, each developed specifically for richness estimates from multiple survey data. We applied these methods to >34,000 samples from >490 studies including from, but not restricted to, animal microbiomes, to arrive at our global estimates, independently of the discussion in S1 Text [1].Third, Wiens’ commentary can yield the impression that we proposed that there are only 40,100 animal-associated bacterial OTUs and that Cephalotes in particular only have 40 associated bacterial OTUs. However, these numbers, mentioned in our S1 Text [1], were not meant to be taken as proposed point estimates for animal-associated OTU richness, and we believe that this was clear from our text. Instead, these numbers were meant as examples to demonstrate how strongly the estimates of animal-associated bacterial richness by Larsen and colleagues [2] would decrease simply by (a) using better justified mathematical formulas, i.e., with the same input data as used by Larsen and colleagues [2] but founded on an actual statistical model; (b) accounting for even minor overlaps in the OTUs associated with different animal genera; and/or (c) using alternative animal diversity estimates published by others [3], rather than those proposed by Larsen and colleagues [2]. Specifically, regarding (b), Larsen and colleagues [2] (pages 233 and 259) performed pairwise host species comparisons within various insect genera (for example, within the Cephalotes) to estimate on average how many bacterial OTUs were unique to each host species, then multiplied that estimate with their estimated number of animal species to determine the global animal-associated bacterial richness. However, since their pairwise host species comparisons were restricted to congeneric species, their estimated number of unique OTUs per host species does not account for potential overlaps between different host genera. Indeed, even if an OTU is only found “in one” Cephalotes species, it might not be truly unique to that host species if it is also present in members of other host genera. To clarify, we did not claim that all animal genera can share bacterial OTUs, but instead considered the implications of some average microbiome overlap (some animal genera might share no bacteria, and other genera might share a lot). The average microbiome overlap of 0.1% (when clustering bacterial 16S sequences into OTUs at 97% similarity) between animal genera used in our illustrative example in S1 Text [1] is of course speculative, but it is not unreasonable (see our next point). A zero overlap (implicitly assumed by Larsen and colleagues [2]) is almost certainly wrong. One goal of our S1 Text [1] was to point out the dramatic effects of such overlaps on animal-associated bacterial richness estimates using “basic” mathematical arguments.Fourth, Wiens’ commentary could yield the impression that existing data are able to tell us with sufficient certainty when a bacterial OTU is “unique” to a specific animal taxon. However, so far, the microbiomes of only a minuscule fraction of animal species have been surveyed. One can thus certainly not exclude the possibility that many bacterial OTUs currently thought to be “unique” to a certain animal taxon are eventually also found in other (potentially distantly related) animal taxa, for example, due to similar host diets and or environmental conditions [4–7]. As a case in point, many bacteria in herbivorous fish guts were found to be closely related to bacteria in mammals [8], and Song and colleagues [6] report that bat microbiomes closely resemble those of birds. The gut microbiome of caterpillars consists mostly of dietary and environmental bacteria and is not species specific [4]. Even in animal taxa with characteristic microbiota, there is a documented overlap across host species and genera. For example, there are a small number of bacteria consistently and specifically associated with bees, but these are found across bee genera at the level of the 99.5% similar 16S rRNA OTUs [5]. To further illustrate that an average microbiome overlap between animal taxa at least as large as the one considered in our S1 Text (0.1%) [1] is not unreasonable, we analyzed 16S rRNA sequences from the Earth Microbiome Project [6,9] and measured the overlap of microbiota originating from individuals of different animal taxa. We found that, on average, 2 individuals from different host classes (e.g., 1 mammalian and 1 avian sample) share 1.26% of their OTUs (16S clustered at 100% similarity), and 2 individuals from different host genera belonging to the same class (e.g., 2 mammalian samples) share 2.84% of their OTUs (methods in S1 Text of this response). A coarser OTU threshold (e.g., 97% similarity, considered in our original paper [1]) would further increase these average overlaps. While less is known about insect microbiomes, there is currently little reason to expect a drastically different picture there, and, as explained in our S1 Text [1], even a small average microbiome overlap of 0.1% between host genera would strongly limit total bacterial richness estimates. The fact that the accumulation curve of detected bacterial OTUs over sampled insect species does not yet strongly level off says little about where the accumulation curve would asymptotically converge; rigorous statistical methods, such as the ones used for our global estimates [1], would be needed to estimate this asymptote.Lastly, we stress that while the present conversation (including previous estimates by Louca and colleagues [1], Larsen and colleagues [2], Locey and colleagues [10], Wiens’ commentary, and this response) focuses on 16S rRNA OTUs, it may well be that at finer phylogenetic resolutions, e.g., at bacterial strain level, host specificity and bacterial richness are substantially higher. In particular, future whole-genome sequencing surveys may well reveal the existence of far more genomic clusters and ecotypes than 16S-based OTUs.     

�� Integrin Tyrosine Phosphorylation Is a Conserved Mechanism for Regulating Talin-induced Integrin Activation

Integrins are large membrane-spanning receptors fundamental to cell adhesion and migration. Integrin adhesiveness for the extracellular matrix is activated by the cytoskeletal protein talin via direct binding of its phosphotyrosine-binding-like F3 domain to the cytoplasmic tail of the β integrin subunit. The phosphotyrosine-binding domain of the signaling protein Dok1, on the other hand, has an inactivating effect on integrins, a phenomenon that is modulated by integrin tyrosine phosphorylation. Using full-length tyrosine-phosphorylated ¹⁵N-labeled β3, β1A, and β7 integrin tails and an NMR-based protein-protein interaction assay, we show that talin1 binds to the NPXY motif and the membrane-proximal portion of β3, β1A, and β7 tails, and that the affinity of this interaction is decreased by integrin tyrosine phosphorylation. Dok1 only interacts weakly with unphosphorylated tails, but its affinity is greatly increased by integrin tyrosine phosphorylation. The Dok1 interaction remains restricted to the integrin NPXY region, thus phosphorylation inhibits integrin activation by increasing the affinity of β integrin tails for a talin competitor that does not form activating membrane-proximal interactions with the integrin. Key residues governing these specificities were identified by detailed structural analysis, and talin1 was engineered to bind preferentially to phosphorylated integrins by introducing the mutation D372R. As predicted, this mutation affects talin1 localization in live cells in an integrin phosphorylation-specific manner. Together, these results indicate that tyrosine phosphorylation is a common mechanism for regulating integrin activation, despite subtle differences in how these integrins interact with their binding proteins.     

Structural diversity in integrin/talin interactions

The adhesion of integrins to the extracellular matrix is regulated by binding of the cytoskeletal protein talin to the cytoplasmic tail of the β-integrin subunit. Structural studies of this interaction have hitherto largely focused on the β3-integrin, one member of the large and diverse integrin family. Here, we employ NMR to probe interactions and dynamics, revealing marked structural diversity in the contacts between β1A, β1D, and β3 tails and the Talin1 and Talin2 isoforms. Coupled with analysis of recent structures of talin/β tail complexes, these studies elucidate the thermodynamic determinants of this heterogeneity and explain why the Talin2/β1D isoforms, which are co-localized in striated muscle, form an unusually tight interaction. We also show that talin/integrin affinity can be enhanced 1000-fold by deleting two residues in the β tail. Together, these studies illustrate how the integrin/talin interaction has been fine-tuned to meet varying biological requirements.     

Analysis of Jmjd6 cellular localization and testing for its involvement in histone demethylation

Background

Methylation of residues in histone tails is part of a network that regulates gene expression. JmjC domain containing proteins catalyze the oxidative removal of methyl groups on histone lysine residues. Here, we report studies to test the involvement of Jumonji domain-containing protein 6 (Jmjd6) in histone lysine demethylation. Jmjd6 has recently been shown to hydroxylate RNA splicing factors and is known to be essential for the differentiation of multiple tissues and cells during embryogenesis. However, there have been conflicting reports as to whether Jmjd6 is a histone-modifying enzyme.

Methodology/Principal Findings

Immunolocalization studies reveal that Jmjd6 is distributed throughout the nucleoplasm outside of regions containing heterochromatic DNA, with occasional localization in nucleoli. During mitosis, Jmjd6 is excluded from the nucleus and reappears in the telophase of the cell cycle. Western blot analyses confirmed that Jmjd6 forms homo-multimers of different molecular weights in the nucleus and cytoplasm. A comparison of mono-, di-, and tri-methylation states of H3K4, H3K9, H3K27, H3K36, and H4K20 histone residues in wildtype and Jmjd6-knockout cells indicate that Jmjd6 is not involved in the demethylation of these histone lysine residues. This is further supported by overexpression of enzymatically active and inactive forms of Jmjd6 and subsequent analysis of histone methylation patterns by immunocytochemistry and western blot analysis. Finally, treatment of cells with RNase A and DNase I indicate that Jmjd6 may preferentially associate with RNA/RNA complexes and less likely with chromatin.

Conclusions/Significance

Taken together, our results provide further evidence that Jmjd6 is unlikely to be involved in histone lysine demethylation. We confirmed that Jmjd6 forms multimers and showed that nuclear localization of the protein involves association with a nucleic acid matrix.