strain WP3 belongs to the group 1 branch of the genus and is a piezotolerant and psychrotolerant species isolated from the deep sea. In this study, a genome-scale model was constructed for WP3 using a combination of genome annotation, ortholog mapping, and physiological verification. The metabolic reconstruction contained 806 genes, 653 metabolites, and 922 reactions, including central metabolic functions that represented nonhomologous replacements between the group 1 and group 2 species. Metabolic simulations with the WP3 model demonstrated consistency with existing knowledge about the physiology of the organism. A comparison of model simulations with experimental measurements verified the predicted growth profiles under increasing concentrations of carbon sources. The WP3 model was applied to study mechanisms of anaerobic respiration through investigating energy conservation, redox balancing, and the generation of proton motive force. Despite being an obligate respiratory organism, WP3 was predicted to use substrate-level phosphorylation as the primary source of energy conservation under anaerobic conditions, a trait previously identified in other species. Further investigation of the ATP synthase activity revealed a positive correlation between the availability of reducing equivalents in the cell and the directionality of the ATP synthase reaction flux. Comparison of the WP3 model with an existing model of a group 2 species, MR-1, revealed that the WP3 model demonstrated greater flexibility in ATP production under the anaerobic conditions. Such flexibility could be advantageous to WP3 for its adaptation to fluctuating availability of organic carbon sources in the deep sea. The well-studied nature of the metabolic diversity of bacteria makes species from this genus a promising platform for investigating the evolution of carbon metabolism and energy conservation. The phylogeny is diverged into two major branches, referred to as group 1 and group 2. While the genotype-phenotype connections of group 2 species have been extensively studied with metabolic modeling, a genome-scale model has been missing for the group 1 species. The metabolic reconstruction of strain WP3 represented the first model for group 1 and the first model among piezotolerant and psychrotolerant deep-sea bacteria. The model brought insights into the mechanisms of energy conservation in WP3 under anaerobic conditions and highlighted its metabolic flexibility in using diverse carbon sources. Overall, the model opens up new opportunities for investigating energy conservation and metabolic adaptation, and it provides a prototype for systems-level modeling of other deep-sea microorganisms.

Forward genetic screens in model organisms have provided important insights into numerous aspects of development, physiology and pathology. With the availability of complete genome sequences and the introduction of RNA-mediated gene interference (RNAi), systematic reverse genetic screens are now also possible. Until now, such genome-wide RNAi screens have mostly been restricted to cultured cells and ubiquitous gene inactivation in Caenorhabditis elegans. This powerful approach has not yet been applied in a tissue-specific manner. Here we report the generation and validation of a genome-wide library of Drosophila melanogaster RNAi transgenes, enabling the conditional inactivation of gene function in specific tissues of the intact organism. Our RNAi transgenes consist of short gene fragments cloned as inverted repeats and expressed using the binary GAL4/UAS system. We generated 22,270 transgenic lines, covering 88% of the predicted protein-coding genes in the Drosophila genome. Molecular and phenotypic assays indicate that the majority of these transgenes are functional. Our transgenic RNAi library thus opens up the prospect of systematically analysing gene functions in any tissue and at any stage of the Drosophila lifespan.

Neural computation in biological and artificial networks relies on nonlinear synaptic integration. The structural connectivity matrix of synaptic weights between neurons is a critical determinant of overall network function. However, quantitative links between neural network structure and function are complex and subtle. For example, many networks can give rise to similar functional responses, and the same network can function differently depending on context. Whether certain patterns of synaptic connectivity are required to generate specific network-level computations is largely unknown. Here we introduce a geometric framework for identifying synaptic connections required by steady-state responses in recurrent networks of rectified-linear neurons. Assuming that the number of specified response patterns does not exceed the number of input synapses, we analytically calculate all feedforward and recurrent connectivity matrices that can generate the specified responses from the network inputs. We then use this analytical characterization to rigorously analyze the solution space geometry and derive certainty conditions guaranteeing a non-zero synapse between neurons. Numerical simulations of feedforward and recurrent networks verify our analytical results. Our theoretical framework could be applied to neural activity data to make anatomical predictions that follow generally from the model architecture. It thus provides novel opportunities for discerning what model features are required to accurately relate neural network structure and function.

In natural environments, animals must efficiently allocate their choices across multiple concurrently available resources when foraging, a complex decision-making process not fully captured by existing models. To understand how rodents learn to navigate this challenge we developed a novel paradigm in which untrained, water-restricted mice were free to sample from six options rewarded at a range of deterministic intervals and positioned around the walls of a large ( 2m) arena. Mice exhibited rapid learning, matching their choices to integrated reward ratios across six options within the first session. A reinforcement learning model with separate states for staying or leaving an option and a dynamic, global learning rate was able to accurately reproduce mouse learning and decision-making. Fiber photometry recordings revealed that dopamine in the nucleus accumbens core (NAcC), but not dorsomedial striatum (DMS), more closely reflected the global learning rate than local error-based updating. Altogether, our results provide insight into the neural substrate of a learning algorithm that allows mice to rapidly exploit multiple options when foraging in large spatial environments.

We identified a novel regulator, Thermococcales glycolytic regulator (Tgr), functioning as both an activator and a repressor of transcription in the hyperthermophilic archaeon Thermococcus kodakaraensis KOD1. Tgr (TK1769) displays similarity (28% identical) to Pyrococcus furiosus TrmB (PF1743), a transcriptional repressor regulating the trehalose/maltose ATP-binding cassette transporter genes, but is more closely related (67%) to a TrmB paralog in P. furiosus (PF0124). Growth of a tgr disruption strain (Deltatgr) displayed a significant decrease in growth rate under gluconeogenic conditions compared with the wild-type strain, whereas comparable growth rates were observed under glycolytic conditions. A whole genome microarray analysis revealed that transcript levels of almost all genes related to glycolysis and maltodextrin metabolism were at relatively high levels in the Deltatgr mutant even under gluconeogenic conditions. The Deltatgr mutant also displayed defects in the transcriptional activation of gluconeogenic genes under these conditions, indicating that Tgr functions as both an activator and a repressor. Genes regulated by Tgr contain a previously identified sequence motif, the Thermococcales glycolytic motif (TGM). The TGM was positioned upstream of the Transcription factor B-responsive element (BRE)/TATA sequence in gluconeogenic promoters and downstream of it in glycolytic promoters. Electrophoretic mobility shift assay indicated that recombinant Tgr protein specifically binds to promoter regions containing a TGM. Tgr was released from the DNA when maltotriose was added, suggesting that this sugar is most likely the physiological effector. Our results strongly suggest that Tgr is a global transcriptional regulator that simultaneously controls, in response to sugar availability, both glycolytic and gluconeogenic metabolism in T. kodakaraensis via its direct binding to the TGM.

Considerable attention has been recently paid to improving replicability and reproducibility in life science research. This has resulted in commendable efforts to standardize a variety of reagents, assays, cell lines and other resources. However, given that microscopy is a dominant tool for biologists, comparatively little discussion has been offered regarding how the proper reporting and documentation of microscopy relevant details should be handled. Image processing is a critical step of almost any microscopy-based experiment; however, improper, or incomplete reporting of its use in the literature is pervasive. The chosen details of an image processing workflow can dramatically determine the outcome of subsequent analyses, and indeed, the overall conclusions of a study. This Review aims to illustrate how proper reporting of image processing methodology improves scientific reproducibility and strengthens the biological conclusions derived from the results.

The taste system is one of our fundamental senses, responsible for detecting and responding to sweet, bitter, umami, salty, and sour stimuli. In the tongue, the five basic tastes are mediated by separate classes of taste receptor cells each finely tuned to a single taste quality. We explored the logic of taste coding in the brain by examining how sweet, bitter, umami, and salty qualities are represented in the primary taste cortex of mice. We used in vivo two-photon calcium imaging to demonstrate topographic segregation in the functional architecture of the gustatory cortex. Each taste quality is represented in its own separate cortical field, revealing the existence of a gustotopic map in the brain. These results expose the basic logic for the central representation of taste.

Olshausen and Field (OF) proposed that neural computations in the primary visual cortex (V1) can be partially modelled by sparse dictionary learning. By minimizing the regularized representation error they derived an online algorithm, which learns Gabor-filter receptive fields from a natural image ensemble in agreement with physiological experiments. Whereas the OF algorithm can be mapped onto the dynamics and synaptic plasticity in a single-layer neural network, the derived learning rule is nonlocal - the synaptic weight update depends on the activity of neurons other than just pre- and postsynaptic ones – and hence biologically implausible. Here, to overcome this problem, we derive sparse dictionary learning from a novel cost-function - a regularized error of the symmetric factorization of the input’s similarity matrix. Our algorithm maps onto a neural network of the same architecture as OF but using only biologically plausible local learning rules. When trained on natural images our network learns Gabor-filter receptive fields and reproduces the correlation among synaptic weights hard-wired in the OF network. Therefore, online symmetric matrix factorization may serve as an algorithmic theory of neural computation.