Optogenetic actuator – ERK biosensor circuits identify MAPK network nodes that shape ERK dynamics

Combining single‐cell measurements of ERK activity dynamics with perturbations provides insights into the MAPK network topology. We built circuits consisting of an optogenetic actuator to activate MAPK signaling and an ERK biosensor to measure single‐cell ERK dynamics. This allowed us to conduct RNAi screens to investigate the role of 50 MAPK proteins in ERK dynamics. We found that the MAPK network is robust against most node perturbations. We observed that the ERK‐RAF and the ERK‐RSK2‐SOS negative feedback operate simultaneously to regulate ERK dynamics. Bypassing the RSK2‐mediated feedback, either by direct optogenetic activation of RAS, or by RSK2 perturbation, sensitized ERK dynamics to further perturbations. Similarly, targeting this feedback in a human ErbB2‐dependent oncogenic signaling model increased the efficiency of a MEK inhibitor. The RSK2‐mediated feedback is thus important for the ability of the MAPK network to produce consistent ERK outputs, and its perturbation can enhance the efficiency of MAPK inhibitors.     

Age‐related memory vulnerability to interfering stimuli is caused by gradual loss of MAPK‐dependent protection in Drosophila

Age‐related memory impairment (AMI) is a common phenomenon across species. Vulnerability to interfering stimuli has been proposed to be an important cause of AMI. However, the molecular mechanisms underlying this vulnerability‐related AMI remain unknown. Here we show that learning‐activated MAPK signals are gradually lost with age, leading to vulnerability‐related AMI in Drosophila. Young flies (2‐ or 3‐day‐old) exhibited a significant increase in phosphorylated MAPK levels within 15 min after learning, whereas aged flies (25‐day‐old) did not. Compared to 3‐day‐old flies, significant 1 h memory impairments were observed in 15‐, 20‐, and 30‐day‐old flies, but not in 10‐day‐old flies. However, with post‐learning interfering stimuli such as cooling or electric stimuli, 10‐day‐old flies had worse memory performance at 1 h than 3‐day‐old flies, showing a premature AMI phenomenon. Increasing learning‐activated MAPK signals through acute transgene expression in mushroom body (MB) neurons restored physiological trace of 1 h memory in a pair of MB output neurons in aged flies. Decreasing such signals in young flies mimicked the impairment of 1 h memory trace in aged flies. Restoring learning‐activated MAPK signals in MB neurons in aged flies significantly suppressed AMI even with interfering stimuli. Thus, our data suggest that age‐related loss of learning‐activated neuronal MAPK signals causes memory vulnerability to interfering stimuli, thereby leading to AMI.     

A Yeast BiFC-seq Method for Genome-wide Interactome Mapping

Genome-wide physical protein±protein interaction(PPI) mapping remains a major challenge for current technologies. Here, we reported a high-efficiency BiFC-seq method, yeastenhanced green fluorescent protein-based bimolecular fluorescence complementation(y EGFPBiFC) coupled with next-generation DNA sequencing, for interactome mapping. We first applied y EGFP-BiFC method to systematically investigate an intraviral network of the Ebola virus.Two-thirds(9/14) of known interactions of EBOV were recap...     

Global Landscape of Native Protein Complexes in Synechocystis sp. PCC 6803

Synechocystis sp. PCC 6803(hereafter: Synechocystis) is a model organism for studying photosynthesis, energy metabolism, and environmental stress. Although known as the first fully sequenced phototrophic organism, Synechocystis still has almost half of its proteome without functional annotations. In this study, by using co-fractionation coupled with liquid chromatographytandem mass spectrometry(LC-MS/MS), we define 291 multi-protein complexes, encompassing24,092 protein±protein interactions(PPIs...     

Battling for Ribosomes: Translational Control at the Forefront of the Antiviral Response

In the early stages of infection, gaining control of the cellular protein synthesis machinery including its ribosomes is the ultimate combat objective for a virus. To successfully replicate, viruses unequivocally need to usurp and redeploy this machinery for translation of their own mRNA. In response, the host triggers global shutdown of translation while paradoxically allowing swift synthesis of antiviral proteins as a strategy to limit collateral damage. This fundamental conflict at the level of translational control defines the outcome of infection. As part of this special issue on molecular mechanisms of early virus–host cell interactions, we review the current state of knowledge regarding translational control during viral infection with specific emphasis on protein kinase RNA-activated and mammalian target of rapamycin-mediated mechanisms. We also describe recent technological advances that will allow unprecedented insight into how viruses and host cells battle for ribosomes.     

CO2 Saturation and Trophic Shift Induced by Microbial Metabolic Processes in a River-Dominated Ocean Margin (Tropical Shallow Lagoon,Chilika, India)

An effort has been made for the first time in Asia's largest brackish water lagoon, Chilika, to investigate the spatio-temporal variability in primary productivity (PP), bacterial productivity (BP), bacterial abundance (BA), bacterial respiration (BR) and bacterial growth efficiency (BGE) in relation to partial pressure of CO₂ (pCO₂) and CO₂ air–water flux and the resultant trophic switchover. Annually, PP ranged between 24 and 376 µg C L^?1 d^?1 with significantly low values throughout the monsoon (MN), caused by light limitation due to inputs of riverine suspended matter. On the contrary, BP and BR ranged from 11.5 to 186.3 µg C L^?1 d^?1 and from 14.1 to 389.4 µg C L^?1 d^?1, respectively, with exceptionally higher values during MN. A wide spatial and temporal variation in the lagoon trophic status was apparent from BP/PP (0.05–6.4) and PP/BR (0.10–18.2) ratios. The seasonal shift in net pelagic production from autotrophy to heterotrophy due to terrestrial organic matter inputs via rivers, enhanced the bacterial metabolism during the MN, as evident from the high pCO₂ (10,134 µatm) and CO₂ air–water flux (714 mm m^?2 d^?1). Large variability in BGE and BP/PP ratios especially during MN led to high bacteria-mediated carbon fluxes which was evident from significantly high bacterial carbon demand (BCD >100% of PP) during this season. This suggested that the net amount of organic carbon (either dissolved or particulate form) synthesized by primary producers in the lagoon was not sufficient to satisfy the bacterial carbon requirements. Lagoon sustained low to moderate autotrophic–heterotrophic coupling with annual mean BCD of 231% relative to the primary production, which depicted that bacterioplankton are the mainstay of the lagoon biogeochemical cycles and principal players that bring changes in trophic status. Study disclosed that the high CO₂ supersaturation and oxygen undersaturation during MN was attributed to the increased heterotrophic respiration (in excess of PP) fuelled by allochthonous organic matter. On a spatial scale, lagoon sectors such as south sector, central sector and outer channel recorded “net autotrophic,” while the northern sector showed “net heterotrophic” throughout the study period.     

Copper-induced germline apoptosis in Caenorhabditis elegans: The independent roles of DNA damage response signaling and the dependent roles of MAPK cascades

Excess copper is toxic to life. Copper has been shown to induce apoptosis in various cell lines and tissues. However, due to the lack of appropriate gene knockout animal models, data concerning the underlying pathways of copper-induced apoptosis are insufficient, especially with regards to in vivo systems. The nematode Caenorhabditis elegans is a good model to study basic biological processes, including stress responses and apoptosis. In the present study, we investigated copper-induced germline apoptosis in the C. elegans strains carrying mutated alleles of homologs to known mammalian genes that are involved in apoptosis regulation. We show here that exposing C. elegans to copper causes dose- and time-dependent germline apoptosis. The knockout of checkpoint genes hus-1, clk-2, the Bcl-2 homolog ced-9, and the BH3-only domain egl-1 did not prevent cells of the germline from copper-induced apoptosis. The loss-of-function of the tumor suppressor gene, p53/cep-1, caused a significant increase in germline apoptosis with exposure to copper, and the depletion of p53 antagonist ABL1 significantly enhanced apoptosis. The knockout of the caspase gene ced-3 and the Apaf-1 homolog ced-4 abrogated both copper-induced and physiological germline apoptosis. Germline apoptosis stopped increase in the strains lin-45(ku51), mek-2(n1989), mpk-1(ku1) under copper stresses, respectively. Copper-induced apoptosis was blocked in the loss-of-function alleles of both JNK and p38 MAPK cascades excepting pmk-3, one of the three p38 MAPK components. Together, the results of this study suggest that caspase and Apaf-1 are required for copper-induced germline apoptosis while DNA damage response genes are not essential, and that the Raf-MEK-ERK, ASK1/2-MKK7-JNK, ASK1/2-MKK3/6-p38 signaling pathways are indispensable in mediating this apoptotic response.     

New versatile cloning and sequencing vectors based on bacteriophage M13

A new pair of cloning and sequencing vectors based on bacteriophage M13mp7 has been developed. These vectors (M13tg130 and M13tg131) contain, in addition to the EcoRI, BamHI, HindIII, SmaI, SalI and PstI sites present in other vectors [cf., M13mp8 and M13mp9, Messing and Vieira, Gene 19 (1982) 269-276], unique restriction recognition sequences for the enzymes EcoRV, KpnI, SphI, SstI and XbaI. A restriction site for the enzyme BglII has been incorporated into the polylinker region of one of the vector pair to permit rapid discrimination between the two vectors.     

Quantitative structure–activity relationship analysis using deep learning based on a novel molecular image input technique

Quantitative structure–activity relationship (QSAR) analysis uses structural, quantum chemical, and physicochemical features calculated from molecular geometry as explanatory variables predicting physiological activity. Recently, deep learning based on advanced artificial neural networks has demonstrated excellent performance in the discipline of QSAR research. While it has properties of feature representation learning that directly calculate feature values from molecular structure, the use of this potential function is limited in QSAR modeling. The present study applied this function of feature representation learning to QSAR analysis by incorporating 360° images of molecular conformations into deep learning. Accordingly, I successfully constructed a highly versatile identification model for chemical compounds that induce mitochondrial membrane potential disruption with the external validation area under the receiver operating characteristic curve of ≥0.9.