Filter
Associated Lab
- Aguilera Castrejon Lab (16) Apply Aguilera Castrejon Lab filter
- Ahrens Lab (64) Apply Ahrens Lab filter
- Aso Lab (40) Apply Aso Lab filter
- Baker Lab (38) Apply Baker Lab filter
- Betzig Lab (112) Apply Betzig Lab filter
- Beyene Lab (13) Apply Beyene Lab filter
- Bock Lab (17) Apply Bock Lab filter
- Branson Lab (52) Apply Branson Lab filter
- Card Lab (41) Apply Card Lab filter
- Cardona Lab (63) Apply Cardona Lab filter
- Chklovskii Lab (13) Apply Chklovskii Lab filter
- Clapham Lab (14) Apply Clapham Lab filter
- Cui Lab (19) Apply Cui Lab filter
- Darshan Lab (12) Apply Darshan Lab filter
- Dennis Lab (1) Apply Dennis Lab filter
- Dickson Lab (46) Apply Dickson Lab filter
- Druckmann Lab (25) Apply Druckmann Lab filter
- Dudman Lab (50) Apply Dudman Lab filter
- Eddy/Rivas Lab (30) Apply Eddy/Rivas Lab filter
- Egnor Lab (11) Apply Egnor Lab filter
- Espinosa Medina Lab (19) Apply Espinosa Medina Lab filter
- Feliciano Lab (7) Apply Feliciano Lab filter
- Fetter Lab (41) Apply Fetter Lab filter
- Fitzgerald Lab (29) Apply Fitzgerald Lab filter
- Freeman Lab (15) Apply Freeman Lab filter
- Funke Lab (38) Apply Funke Lab filter
- Gonen Lab (91) Apply Gonen Lab filter
- Grigorieff Lab (62) Apply Grigorieff Lab filter
- Harris Lab (60) Apply Harris Lab filter
- Heberlein Lab (94) Apply Heberlein Lab filter
- Hermundstad Lab (26) Apply Hermundstad Lab filter
- Hess Lab (76) Apply Hess Lab filter
- Ilanges Lab (2) Apply Ilanges Lab filter
- Jayaraman Lab (46) Apply Jayaraman Lab filter
- Ji Lab (33) Apply Ji Lab filter
- Johnson Lab (6) Apply Johnson Lab filter
- Kainmueller Lab (19) Apply Kainmueller Lab filter
- Karpova Lab (14) Apply Karpova Lab filter
- Keleman Lab (13) Apply Keleman Lab filter
- Keller Lab (76) Apply Keller Lab filter
- Koay Lab (18) Apply Koay Lab filter
- Lavis Lab (148) Apply Lavis Lab filter
- Lee (Albert) Lab (34) Apply Lee (Albert) Lab filter
- Leonardo Lab (23) Apply Leonardo Lab filter
- Li Lab (28) Apply Li Lab filter
- Lippincott-Schwartz Lab (167) Apply Lippincott-Schwartz Lab filter
- Liu (Yin) Lab (6) Apply Liu (Yin) Lab filter
- Liu (Zhe) Lab (61) Apply Liu (Zhe) Lab filter
- Looger Lab (138) Apply Looger Lab filter
- Magee Lab (49) Apply Magee Lab filter
- Menon Lab (18) Apply Menon Lab filter
- Murphy Lab (13) Apply Murphy Lab filter
- O'Shea Lab (6) Apply O'Shea Lab filter
- Otopalik Lab (13) Apply Otopalik Lab filter
- Pachitariu Lab (47) Apply Pachitariu Lab filter
- Pastalkova Lab (18) Apply Pastalkova Lab filter
- Pavlopoulos Lab (19) Apply Pavlopoulos Lab filter
- Pedram Lab (15) Apply Pedram Lab filter
- Podgorski Lab (16) Apply Podgorski Lab filter
- Reiser Lab (51) Apply Reiser Lab filter
- Riddiford Lab (44) Apply Riddiford Lab filter
- Romani Lab (43) Apply Romani Lab filter
- Rubin Lab (143) Apply Rubin Lab filter
- Saalfeld Lab (63) Apply Saalfeld Lab filter
- Satou Lab (16) Apply Satou Lab filter
- Scheffer Lab (36) Apply Scheffer Lab filter
- Schreiter Lab (67) Apply Schreiter Lab filter
- Sgro Lab (21) Apply Sgro Lab filter
- Shroff Lab (30) Apply Shroff Lab filter
- Simpson Lab (23) Apply Simpson Lab filter
- Singer Lab (80) Apply Singer Lab filter
- Spruston Lab (93) Apply Spruston Lab filter
- Stern Lab (156) Apply Stern Lab filter
- Sternson Lab (54) Apply Sternson Lab filter
- Stringer Lab (34) Apply Stringer Lab filter
- Svoboda Lab (135) Apply Svoboda Lab filter
- Tebo Lab (33) Apply Tebo Lab filter
- Tervo Lab (9) Apply Tervo Lab filter
- Tillberg Lab (21) Apply Tillberg Lab filter
- Tjian Lab (64) Apply Tjian Lab filter
- Truman Lab (88) Apply Truman Lab filter
- Turaga Lab (50) Apply Turaga Lab filter
- Turner Lab (37) Apply Turner Lab filter
- Vale Lab (7) Apply Vale Lab filter
- Voigts Lab (3) Apply Voigts Lab filter
- Wang (Meng) Lab (17) Apply Wang (Meng) Lab filter
- Wang (Shaohe) Lab (25) Apply Wang (Shaohe) Lab filter
- Wu Lab (9) Apply Wu Lab filter
- Zlatic Lab (28) Apply Zlatic Lab filter
- Zuker Lab (25) Apply Zuker Lab filter
Associated Project Team
- CellMap (12) Apply CellMap filter
- COSEM (3) Apply COSEM filter
- FIB-SEM Technology (2) Apply FIB-SEM Technology filter
- Fly Descending Interneuron (10) Apply Fly Descending Interneuron filter
- Fly Functional Connectome (14) Apply Fly Functional Connectome filter
- Fly Olympiad (5) Apply Fly Olympiad filter
- FlyEM (53) Apply FlyEM filter
- FlyLight (49) Apply FlyLight filter
- GENIE (45) Apply GENIE filter
- Integrative Imaging (3) Apply Integrative Imaging filter
- Larval Olympiad (2) Apply Larval Olympiad filter
- MouseLight (18) Apply MouseLight filter
- NeuroSeq (1) Apply NeuroSeq filter
- ThalamoSeq (1) Apply ThalamoSeq filter
- Tool Translation Team (T3) (26) Apply Tool Translation Team (T3) filter
- Transcription Imaging (49) Apply Transcription Imaging filter
Publication Date
- 2025 (81) Apply 2025 filter
- 2024 (223) Apply 2024 filter
- 2023 (162) Apply 2023 filter
- 2022 (193) Apply 2022 filter
- 2021 (194) Apply 2021 filter
- 2020 (196) Apply 2020 filter
- 2019 (202) Apply 2019 filter
- 2018 (232) Apply 2018 filter
- 2017 (217) Apply 2017 filter
- 2016 (209) Apply 2016 filter
- 2015 (252) Apply 2015 filter
- 2014 (236) Apply 2014 filter
- 2013 (194) Apply 2013 filter
- 2012 (190) Apply 2012 filter
- 2011 (190) Apply 2011 filter
- 2010 (161) Apply 2010 filter
- 2009 (158) Apply 2009 filter
- 2008 (140) Apply 2008 filter
- 2007 (106) Apply 2007 filter
- 2006 (92) Apply 2006 filter
- 2005 (67) Apply 2005 filter
- 2004 (57) Apply 2004 filter
- 2003 (58) Apply 2003 filter
- 2002 (39) Apply 2002 filter
- 2001 (28) Apply 2001 filter
- 2000 (29) Apply 2000 filter
- 1999 (14) Apply 1999 filter
- 1998 (18) Apply 1998 filter
- 1997 (16) Apply 1997 filter
- 1996 (10) Apply 1996 filter
- 1995 (18) Apply 1995 filter
- 1994 (12) Apply 1994 filter
- 1993 (10) Apply 1993 filter
- 1992 (6) Apply 1992 filter
- 1991 (11) Apply 1991 filter
- 1990 (11) Apply 1990 filter
- 1989 (6) Apply 1989 filter
- 1988 (1) Apply 1988 filter
- 1987 (7) Apply 1987 filter
- 1986 (4) Apply 1986 filter
- 1985 (5) Apply 1985 filter
- 1984 (2) Apply 1984 filter
- 1983 (2) Apply 1983 filter
- 1982 (3) Apply 1982 filter
- 1981 (3) Apply 1981 filter
- 1980 (1) Apply 1980 filter
- 1979 (1) Apply 1979 filter
- 1976 (2) Apply 1976 filter
- 1973 (1) Apply 1973 filter
- 1970 (1) Apply 1970 filter
- 1967 (1) Apply 1967 filter
Type of Publication
4072 Publications
Showing 1191-1200 of 4072 resultsThe microtubule cytoskeleton has proven to be an effective target for cancer therapeutics. One class of drugs, known as microtubule stabilizing agents (MSAs), binds to microtubule polymers and stabilizes them against depolymerization. The prototype of this group of drugs, Taxol, is an effective chemotherapeutic agent used extensively in the treatment of human ovarian, breast, and lung carcinomas. Although electron crystallography and photoaffinity labeling experiments determined that the binding site for Taxol is in a hydrophobic pocket in beta-tubulin, little was known about the effects of this drug on the conformation of the entire microtubule. A recent study from our laboratory utilizing hydrogen-deuterium exchange (HDX) in concert with various mass spectrometry (MS) techniques has provided new information on the structure of microtubules upon Taxol binding. In the current study we apply this technique to determine the binding mode and the conformational effects on chicken erythrocyte tubulin (CET) of another MSA, discodermolide, whose synthetic analogues may have potential use in the clinic. We confirmed that, like Taxol, discodermolide binds to the taxane binding pocket in beta-tubulin. However, as opposed to Taxol, which has major interactions with the M-loop, discodermolide orients itself away from this loop and toward the N-terminal H1-S2 loop. Additionally, discodermolide stabilizes microtubules mainly via its effects on interdimer contacts, specifically on the alpha-tubulin side, and to a lesser extent on interprotofilament contacts between adjacent beta-tubulin subunits. Also, our results indicate complementary stabilizing effects of Taxol and discodermolide on the microtubules, which may explain the synergy observed between the two drugs in vivo.
The orthogonal array of axon pathways in the Drosophila CNS is constructed in part under the control of three Robo family axon guidance receptors: Robo1, Robo2 and Robo3. Each of these receptors is responsible for a distinct set of guidance decisions. To determine the molecular basis for these functional specializations, we used homologous recombination to create a series of 9 "robo swap" alleles: expressing each of the three Robo receptors from each of the three robo loci. We demonstrate that the lateral positioning of longitudinal axon pathways relies primarily on differences in gene regulation, not distinct combinations of Robo proteins as previously thought. In contrast, specific features of the Robo1 and Robo2 proteins contribute to their distinct functions in commissure formation. These specializations allow Robo1 to prevent crossing and Robo2 to promote crossing. These data demonstrate how diversification of expression and structure within a single family of guidance receptors can shape complex patterns of neuronal wiring.
The superficial superior colliculus (sSC) occupies a critical node in the mammalian visual system; it is one of two major retinorecipient areas, receives visual cortical input, and innervates visual thalamocortical circuits. Nonetheless, the contribution of sSC neurons to downstream neural activity and visually guided behavior is unknown and frequently neglected. Here we identified the visual stimuli to which specific classes of sSC neurons respond, the downstream regions they target, and transgenic mice enabling class-specific manipulations. One class responds to small, slowly moving stimuli and projects exclusively to lateral posterior thalamus; another, comprising GABAergic neurons, responds to the sudden appearance or rapid movement of large stimuli and projects to multiple areas, including the lateral geniculate nucleus. A third class exhibits direction-selective responses and targets deeper SC layers. Together, our results show how specific sSC neurons represent and distribute diverse information and enable direct tests of their functional role.
Although anatomical, lesion, and imaging studies of the hippocampus indicate qualitatively different information processing along its septo-temporal axis, physiological mechanisms supporting such distinction are missing. We found fundamental differences between the dorsal (dCA3) and the ventral-most parts (vCA3) of the hippocampus in both environmental representation and temporal dynamics. Discrete place fields of dCA3 neurons evenly covered all parts of the testing environments. In contrast, vCA3 neurons (1) rarely showed continuous two-dimensional place fields, (2) differentiated open and closed arms of a radial maze, and (3) discharged similar firing patterns with respect to the goals, both on multiple arms of a radial maze and during opposite journeys in a zigzag maze. In addition, theta power and the fraction of theta-rhythmic neurons were substantially reduced in the ventral compared with dorsal hippocampus. We hypothesize that the spatial representation in the septo-temporal axis of the hippocampus is progressively decreased. This change is paralleled with a reduction of theta rhythm and an increased representation of nonspatial information.
Huntington's disease (HD) is a neurodegenerative disorder caused by a CAG trinucleotide repeat expansion in the first exon of the huntingtin gene. The huntingtin protein (Htt) is ubiquitously expressed and localized in several organelles, including endosomes, where it plays an essential role in intracellular trafficking. Presymptomatic HD is associated with a failure in energy metabolism and oxidative stress. Ascorbic acid is a potent antioxidant that plays a key role in modulating neuronal metabolism and is highly concentrated in the brain. During synaptic activity, neurons take up ascorbic acid released by glial cells; however, this process is disrupted in HD. In this study, we aim to elucidate the molecular and cellular mechanisms underlying this dysfunction. Using an electrophysiological approach in presymptomatic YAC128 HD slices, we observed decreased ascorbic acid flux from astrocytes to neurons, which altered neuronal metabolic substrate preferences. Ascorbic acid efflux and recycling were also decreased in cultured astrocytes from YAC128 HD mice. We confirmed our findings using GFAP-HD160Q, an HD mice model expressing mutant N-terminal Htt mainly in astrocytes. For the first time, we demonstrated that ascorbic acid is released from astrocytes via extracellular vesicles (EVs). Decreased number of particles and exosomal markers were observed in EV fractions from cultured YAC128 HD astrocytes and Htt-KD cells. We observed reduced number of multivesicular bodies (MVBs) in YAC128 HD striatum via electron microscopy, suggesting mutant Htt alters MVB biogenesis. EVs containing ascorbic acid effectively reduced reactive oxygen species, whereas "free" ascorbic acid played a role in modulating neuronal metabolic substrate preferences. These findings suggest that the early redox imbalance observed in HD arises from a reduced release of ascorbic acid-containing EVs by astrocytes. Meanwhile, a decrease in "free" ascorbic acid likely contributes to presymptomatic metabolic impairment.
Representation learning in neural networks may be implemented with supervised or unsupervised algorithms, distinguished by the availability of feedback. In sensory cortex, perceptual learning drives neural plasticity, but it is not known if this is due to supervised or unsupervised learning. Here we recorded populations of up to 90,000 neurons simultaneously from the primary visual cortex (V1) and higher visual areas (HVA), while mice learned multiple tasks as well as during unrewarded exposure to the same stimuli. Similar to previous studies, we found that neural changes in task mice were correlated with their behavioral learning. However, the neural changes were mostly replicated in mice with unrewarded exposure, suggesting that the changes were in fact due to unsupervised learning. The neural plasticity was concentrated in the medial HVAs and obeyed visual, rather than spatial, learning rules. In task mice only, we found a ramping reward prediction signal in anterior HVAs, potentially involved in supervised learning. Our neural results predict that unsupervised learning may accelerate subsequent task learning, a prediction which we validated with behavioral experiments.
Renal peritubular interstitial fibroblast-like cells are critical for adult erythropoiesis, as they are the main source of erythropoietin (EPO). Hypoxia-inducible factor 2 (HIF-2) controls EPO synthesis in the kidney and liver and is regulated by prolyl-4-hydroxylase domain (PHD) dioxygenases PHD1, PHD2, and PHD3, which function as cellular oxygen sensors. Renal interstitial cells with EPO-producing capacity are poorly characterized, and the role of the PHD/HIF-2 axis in renal EPO-producing cell (REPC) plasticity is unclear. Here we targeted the PHD/HIF-2/EPO axis in FOXD1 stroma-derived renal interstitial cells and examined the role of individual PHDs in REPC pool size regulation and renal EPO output. Renal interstitial cells with EPO-producing capacity were entirely derived from FOXD1-expressing stroma, and Phd2 inactivation alone induced renal Epo in a limited number of renal interstitial cells. EPO induction was submaximal, as hypoxia or pharmacologic PHD inhibition further increased the REPC fraction among Phd2-/- renal interstitial cells. Moreover, Phd1 and Phd3 were differentially expressed in renal interstitium, and heterozygous deficiency for Phd1 and Phd3 increased REPC numbers in Phd2-/- mice. We propose that FOXD1 lineage renal interstitial cells consist of distinct subpopulations that differ in their responsiveness to Phd2 inactivation and thus regulation of HIF-2 activity and EPO production under hypoxia or conditions of pharmacologic or genetic PHD inactivation.
The transition between dormant and active Mycobacterium tuberculosis infection requires reorganization of its lipid metabolism and activation of a battery of serine hydrolase enzymes. Among these serine hydrolases, Rv0045c is a mycobacterial-specific serine hydrolase with limited sequence homology outside mycobacteria but structural homology to divergent bacterial hydrolase families. Herein, we determined the global substrate specificity of Rv0045c against a library of fluorogenic hydrolase substrates, constructed a combined experimental and computational model for its binding pocket, and performed comprehensive substitutional analysis to develop a structural map of its binding pocket. Rv0045c showed strong substrate selectivity toward short, straight chain alkyl esters with the highest activity toward four atom chains. This strong substrate preference was maintained through the combined action of residues in a flexible loop connecting the cap and α/β hydrolase domains and in residues close to the catalytic triad. Two residues bracketing the substrate-binding pocket (Gly90 and His187) were essential to maintaining the narrow substrate selectivity of Rv0045c toward various acyl ester substituents, as independent conversion of these residues significantly increased its catalytic activity and broadened its substrate specificity. Focused saturation mutagenesis of position 187 implicated this residue, as the differentiation point between the substrate specificity of Rv0045c and the structurally homologous ybfF hydrolase family. Insertion of the analogous tyrosine residue from ybfF hydrolases into Rv0045c increased the catalytic activity of Rv0045 by over 20-fold toward diverse ester substrates. The unique binding pocket structure and selectivity of Rv0045c provide molecular indications of its biological role and evidence for expanded substrate diversity in serine hydrolases from M. tuberculosis.
A general method to recognize and track unmarked animals within a population will enable new studies of social behavior and individuality.
How do descending inputs from the brain control leg motor circuits to change how an animal walks? Conceptually, descending neurons are thought to function either as command-type neurons, in which a single type of descending neuron exerts a high-level control to elicit a coordinated change in motor output, or through a population coding mechanism, whereby a group of neurons, each with local effects, act in combination to elicit a global motor response. The Drosophila Moonwalker Descending Neurons (MDNs), which alter leg motor circuit dynamics so that the fly walks backwards, exemplify the command-type mechanism. Here, we identify several dozen MDN target neurons within the leg motor circuits, and show that two of them mediate distinct and highly-specific changes in leg muscle activity during backward walking: LBL40 neurons provide the hindleg power stroke during stance phase; LUL130 neurons lift the legs at the end of stance to initiate swing. Through these two effector neurons, MDN directly controls both the stance and swing phases of the backward stepping cycle. These findings suggest that command-type descending neurons can also operate through the distributed control of local motor circuits.