Project description:Drought represents a significant stress to microorganisms and is known to reduce microbial activity and organic matter decomposition in Mediterranean ecosystems. However, we lack a detailed understanding of the drought stress response of microbial decomposers. Here we present metatranscriptomic data on the physiological response of in situ microbial communities on plant litter to long-term drought in Californian grass and shrub ecosystems.
Project description:Cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) plays a pivotal role in preventing autoimmunity and fostering anticancer immunity by interacting with B7 proteins CD80 and CD86. CTLA-4 is the first immune checkpoint targeted with a monoclonal antibody inhibitor. Checkpoint inhibitors have generated durable responses in many cancer patients, representing a revolutionary milestone in cancer immunotherapy. However, therapeutic efficacy is limited to a small portion of patients, and immune-related adverse events are noteworthy, especially for monoclonal antibodies directed against CTLA-4. Previously, small molecules have been developed to impair the CTLA-4: CD80 interaction; however, they directly targeted CD80 and not CTLA-4. In this study, we performed artificial intelligence (AI)-powered virtual screening of approximately ten million compounds to target CTLA-4. We validated primary hits with biochemical, biophysical, immunological, and experimental animal assays. We then optimized lead compounds and obtained inhibitors with an inhibitory concentration of 1 micromole in disrupting the interaction between CTLA-4 and CD80. Unlike ipilimumab, these small molecules did not degrade CTLA-4. Several compounds inhibited tumor development prophylactically and therapeutically in syngeneic and CTLA-4-humanized mice. This project supports an AI-based framework in designing small molecules targeting immune checkpoints for cancer therapy.
2024-02-15 | GSE228560 | GEO
Project description:Assembled contigs of 760 MetaHIT metagenomes
Project description:We propose a model of a learning agent whose interaction with the environment is governed by a simulation-based projection, which allows the agent to project itself into future situations before it takes real action. Projective simulation is based on a random walk through a network of clips, which are elementary patches of episodic memory. The network of clips changes dynamically, both due to new perceptual input and due to certain compositional principles of the simulation process. During simulation, the clips are screened for specific features which trigger factual action of the agent. The scheme is different from other, computational, notions of simulation, and it provides a new element in an embodied cognitive science approach to intelligent action and learning. Our model provides a natural route for generalization to quantum-mechanical operation and connects the fields of reinforcement learning and quantum computation.
Project description:In the recent years, RNA silencing has been studied extensively to be a conserved regulatory process in plants. In the antiviral silencing, the intermediate double-stranded RNA form during the replication of RNA viruses were recognized and processed into abundant of overlapping viral siRNA (viRNAs). Accordingly, the cloned viRNAs could be conversely assembled into some contigs of viruses, which is recently exploited for identifying new viruses and their genome sequences.To obtain rapidly the complete genome sequence of BYSMV, we carried out deep sequencing of small RNAs from healthy and BYSMV infected wheat, respectively. Thirteen contigs were assembled from the overlapping viRNAs only present in the infected wheat but not in the healthy wheat. The results of BLAST showed that ten contigs shared about 96% identity with the reported L gene of BYSMV isolate Zanjan-1.
Project description:Ocean metaproteomics provides valuable insights into the structure and function of marine microbial communities. Yet, ocean samples are challenging due to their extensive biological diversity that results in a very large number of peptides with a large dynamic range. This study characterized the capabilities of data independent acquisition (DIA) mode for use in ocean metaproteomic samples. Spectral libraries were constructed from discovered peptides and proteins using machine learning algorithms to remove incorporation of false positives in the libraries. When compared with 1-dimensional and 2-dimensional data dependent acquisition analyses (DDA), DIA outperformed DDA both with and without gas phase fractionation. We found that larger discovered protein spectral libraries performed better, regardless of the geographic distance between where samples were collected for library generation and where the test samples were collected. Moreover, the spectral library containing all unique proteins present in the Ocean Protein Portal outperformed smaller libraries generated from individual sampling campaigns. However, a spectral library constructed from all open reading frames in a metagenome was found to be too large to be workable, resulting in low peptide identifications due to challenges maintaining a low false discovery rate with such a large database size. Given sufficient sequencing depth and validation studies, spectral libraries generated from previously discovered proteins can serve as a community resource, saving resequencing efforts. The spectral libraries generated in this study are available at the Ocean Protein Portal for this purpose.
Project description:In the recent years, RNA silencing has been studied extensively to be a conserved regulatory process in plants. In the antiviral silencing, the intermediate double-stranded RNA form during the replication of RNA viruses were recognized and processed into abundant of overlapping viral siRNA (viRNAs). Accordingly, the cloned viRNAs could be conversely assembled into some contigs of viruses, which is recently exploited for identifying new viruses and their genome sequences.To obtain rapidly the complete genome sequence of BYSMV, we carried out deep sequencing of small RNAs from healthy and BYSMV infected wheat, respectively. Thirteen contigs were assembled from the overlapping viRNAs only present in the infected wheat but not in the healthy wheat. The results of BLAST showed that ten contigs shared about 96% identity with the reported L gene of BYSMV isolate Zanjan-1. Viral assembly from the BYSMV infected wheat plants to obtain the full lengh genome and characterise the viral siRNAs
Project description:Small RNA libraries were constructed from total RNA from Jasminum sambac plants exhibiting virus-like symptoms. After sequencing, small RNAs were assembled into contigs with MetaVelvet and assembled contigs were aligned against the NR database of NCBI using BLASTx. Top hits that reported a virus as subject were considered putative viral sequences. Based on such alignments, the whole genome of a virus, we tentatively name Jasmine Virus H was recovered and cloned. Two more small RNA libraries were made in a confirmatory experiment. One from Jasminum sambac and another one from Nicotiana benthamiana plants infected with the newly-cloned virus. The small RNA libraries were aligned against the full-length sequence of Jasmine Virus H to determine the spacial distribution of virus-derived small RNAs along the virus genome.