SUMMARY Overnutrition is associated with chronic inflammation in metabolic tissues; however, whether metabolic inflammation compromises the neural regulatory systems and therefore promotes overnutrition-associated diseases remains unexplored. Our results demonstrate that a mediator of metabolic inflammation, IKKβ/NF-κB, normally remains inactive although enriched in the hypothalamic neurons; however, overnutrition atypically activates hypothalamic IKKβ/NF-κB at least through elevated endoplasmic reticulum stress in the hypothalamus. While forced activation of hypothalamic IKKβ/NF-κB interrupts central insulin/leptin signaling and actions, site- or cell-specific suppression of IKKβ either broadly across the brain, or locally within the mediobasal hypothalamus, or specifically in hypothalamic AGRP neurons significantly protects against obesity and glucose intolerance. The involved molecular mechanisms include the control of IKKβ/NF-κB over SOCS3, a core inhibitor of insulin and leptin signaling. In conclusion, the hypothalamic IKKβ/NF-κB program is a general neural mechanism for energy imbalance underlying obesity; suppressing hypothalamic IKKβ/NF-κB represents a new strategy to combat obesity and related diseases.

While deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta. Here we describe a deep learning–based protein sequence design method, ProteinMPNN, with outstanding performance in both in silico and experimental tests. On native protein backbones, ProteinMPNN has a sequence recovery of 52.4%, compared to 32.9% for Rosetta. The amino acid sequence at different positions can be coupled between single or multiple chains, enabling application to a wide range of current protein design challenges. We demonstrate the broad utility and high accuracy of ProteinMPNN using X-ray crystallography, cryoEM and functional studies by rescuing previously failed designs, made using Rosetta or AlphaFold, of protein monomers, cyclic homo-oligomers, tetrahedral nanoparticles, and target binding proteins.

Hypothalamic neuropeptides play essential roles in regulating energy and body weight balance. Energy imbalance and obesity have been linked to hypothalamic signaling defects in regulating neuropeptide genes; however, it is unknown whether dysregulation of neuropeptide exocytosis could be critically involved. This study discovered that synaptotagmin-4, an atypical modulator of synaptic exocytosis, is expressed most abundantly in oxytocin neurons of the hypothalamus. Synaptotagmin-4 negatively regulates oxytocin exocytosis, and dietary obesity is associated with increased vesicle binding of synaptotagmin-4 and thus enhanced negative regulation of oxytocin release. Overexpressing synaptotagmin-4 in hypothalamic oxytocin neurons and centrally antagonizing oxytocin in mice are similarly obesogenic. Synaptotagmin-4 inhibition prevents against dietary obesity by normalizing oxytocin release and energy balance under chronic nutritional excess. In conclusion, the negative regulation of synaptotagmin-4 on oxytocin release represents a hypothalamic basis of neuropeptide exocytosis in controlling obesity and related diseases.

Synaptotagmin (syt) 7 is one of three syt isoforms found in all metazoans; it is ubiquitously expressed, yet its function in neurons remains obscure. Here, we resolved Ca2+-dependent and Ca2+-independent synaptic vesicle (SV) replenishment pathways, and found that syt 7 plays a selective and critical role in the Ca2+-dependent pathway. Mutations that disrupt Ca2+-binding to syt 7 abolish this function, suggesting that syt 7 functions as a Ca2+-sensor for replenishment. The Ca2+-binding protein calmodulin (CaM) has also been implicated in SV replenishment, and we found that loss of syt 7 was phenocopied by a CaM antagonist. Moreover, we discovered that syt 7 binds to CaM in a highly specific and Ca2+-dependent manner; this interaction requires intact Ca2+-binding sites within syt 7. Together, these data indicate that a complex of two conserved Ca2+-binding proteins, syt 7 and CaM, serve as a key regulator of SV replenishment in presynaptic nerve terminals.DOI: http://dx.doi.org/10.7554/eLife.01524.001

We developed an integrated chip for real-time amplification and detection of nucleic acid using pH-sensing complementary metal-oxide semiconductor (CMOS) technology. Here we show an amplification-coupled detection method for directly measuring released hydrogen ions during nucleotide incorporation rather than relying on indirect measurements such as fluorescent dyes. This is a label-free, non-optical, real-time method for detecting and quantifying target sequences by monitoring pH signatures of native amplification chemistries. The chip has ion-sensitive field effect transistor (ISFET) sensors, temperature sensors, resistive heating, signal processing and control circuitry all integrated to create a full system-on-chip platform. We evaluated the platform using two amplification strategies: PCR and isothermal amplification. Using this platform, we genotyped and discriminated unique single-nucleotide polymorphism (SNP) variants of the cytochrome P450 family from crude human saliva. We anticipate this semiconductor technology will enable the creation of devices for cost-effective, portable and scalable real-time nucleic acid analysis.

Rapid and specific detection of avian influenza virus (AIV) is urgently needed due to the concerns over the potential outbreaks of highly pathogenic H5N1 influenza in animals and humans. Aptamers are artificial oligonucleic acids that can bind specific target molecules, and show comparable affinity for target viruses and better thermal stability than monoclonal antibodies. The objective of this research was to use a DNA-aptamer as the specific recognition element in a portable Surface Plasmon Resonance (SPR) biosensor for rapid detection of AIV H5N1 in poultry swab samples. A SPR biosensor was fabricated using selected aptamers that were biotinylated and then immobilized on the sensor gold surface coated with streptavidin via streptavidin-biotin binding. The immobilized aptamers captured AIV H5N1 in a sample solution, which caused an increase in the refraction index (RI). After optimizing the streptavidin and aptamer parameters, the results showed that the RI value was linearly related (R2 = 0.99) to the concentration of AIV in the range of 0.128 to 1.28 HAU. Negligible signal (<4% of H5N1) was observed from six non-target AIV subtypes. The AIV H5N1 in poultry swab samples with concentrations of 0.128 to 12.8 HAU could be detected using this aptasensor in 1.5 h.

Protein pores play key roles in fundamental biological processes 1 and biotechnological applications such as DNA nanopore sequencing 2 – 4 , and hence the design of pore-containing proteins is of considerable scientific and biotechnological interest. Synthetic amphiphilic peptides have been found to form ion channels 5 , 6 , and there have been recent advances in de novo membrane protein design 7 , 8 and in redesigning naturally occurring channel-containing proteins 9 , 10 . However, the de novo design of stable, well-defined transmembrane protein pores capable of conducting ions selectively or large enough to allow passage of small-molecule fluorophores remains an outstanding challenge 11 , 12 . Here, we report the computational design of protein pores formed by two concentric rings of ɑ-helices that are stable and mono-disperse in both water-soluble and transmembrane forms. Crystal structures of the water-soluble forms of a 12 helical and a 16 helical pore are close to the computational design models. Patch-clamp electrophysiology experiments show that the transmembrane form of the 12-helix pore expressed in insect cells allows passage of ions across the membrane with high selectivity for potassium over sodium, which is blocked by specific chemical modification at the pore entrance. The transmembrane form of the 16-helix pore, but not the 12-helix pore, allows passage of biotinylated Alexa Fluor 488 when incorporated into liposomes using in vitro protein synthesis. A cryo-EM structure of the 16-helix transmembrane pore closely matches the design model. The ability to produce structurally and functionally well-defined transmembrane pores opens the door to the creation of designer pores for a wide variety of applications.

While deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta. Here we describe a deep learning based protein sequence design method, ProteinMPNN, with outstanding performance in both in silico and experimental tests. The amino acid sequence at different positions can be coupled between single or multiple chains, enabling application to a wide range of current protein design challenges. On native protein backbones, ProteinMPNN has a sequence recovery of 52.4%, compared to 32.9% for Rosetta. Incorporation of noise during training improves sequence recovery on protein structure models, and produces sequences which more robustly encode their structures as assessed using structure prediction algorithms. We demonstrate the broad utility and high accuracy of ProteinMPNN using X-ray crystallography, cryoEM and functional studies by rescuing previously failed designs, made using Rosetta or AlphaFold, of protein monomers, cyclic homo-oligomers, tetrahedral nanoparticles, and target binding proteins.