Epidemic methicillin-resistant Staphylococcus aureus (MRSA) imposes an increasing impact on public health. Due to multi-antibiotics resistance in MRSA strains, there is an urgent need to develop novel therapeutics such as effective monoclonal antibodies (mAbs) against MRSA infections. Staphylococcus aureus surface protein A (SasA), a large surface-located protein (~240 kDa), is one of MSCRAMMs (microbial surface components recognizing adhesive matrix molecules) and a potential target for immunotherapeutic approaches against S. aureus infections. In the present study, we analyzed the sequence of SasA with bioinformatics tools and generated a protective monoclonal antibody (2H7) targeting the conserved domain of SasA. 2H7 was shown to recognize wild-type S. aureus and promote opsonophagocytic killing of S. aureus. In both sepsis and peritoneal infection models, prophylactic administration of 2H7 improved the survival of BALB/c mice challenged by S. aureus strain USA300 and ST239 (prevalent MRSA clones in North America and Asian countries, respectively) and enhanced bacterial clearance in kidneys. Additionally, 2H7 prophylaxis prevented the formation of intraperitoneal abscess in a murine model of peritoneal infection and therapeutic administration of 2H7 showed protective efficacy in a murine sepsis model. Our results presented here provide supporting evidences that an anti-SasA mAb might be a potential component in an antibody-based immunotherapeutic treatment of MRSA infections.

About ten years ago, HMAX was proposed as a simple and biologically feasible model for object recognition, based on how the visual cortex processes information. However, the model does not encompass sparse firing, which is a hallmark of neurons at all stages of the visual pathway. The current paper presents an improved model, called sparse HMAX, which integrates sparse firing. This model is able to learn higher-level features of objects on unlabeled training images. Unlike most other deep learning models that explicitly address global structure of images in every layer, sparse HMAX addresses local to global structure gradually along the hierarchy by applying patch-based learning to the output of the previous layer. As a consequence, the learning method can be standard sparse coding (SSC) or independent component analysis (ICA), two techniques deeply rooted in neuroscience. What makes SSC and ICA applicable at higher levels is the introduction of linear higher-order statistical regularities by max pooling. After training, high-level units display sparse, invariant selectivity for particular individuals or for image categories like those observed in human inferior temporal cortex (ITC) and medial temporal lobe (MTL). Finally, on an image classification benchmark, sparse HMAX outperforms the original HMAX by a large margin, suggesting its great potential for computer vision.

Yersinia pestis (Y. pestis) has caused an alarming number of deaths throughout recorded human history, and novel prophylactics and therapeutics are necessary given its potential as a bioweapon. Only one monoclonal antibody has been identified to date that provides complete protection against Y. pestis. Here, we describe a second novel murine monoclonal antibody (F2H5) that provided complete protection against Y. pestis 141 infection when administered prophylactically to Balb/c mice (100 μg intravenously). We humanized F2H5, characterized its ability to bind to the Y. pestis F1 protein and further characterized the neutralizing epitope using computational and experimental approaches. While Western blot results suggested a linear epitope, peptide mapping using ELISA failed to identify an epitope, suggesting a conformational epitope instead. We adopted a computational approach based on Residue Contact Frequency to predict the site of antigen-antibody interaction and defined the F2H5/F1 binding site computationally. Based on computational approach, we determined that residues G104E105N106 in F1 were critical to F2H5 binding and that CDRH2 and CDRH3 of F2H5 interacted with F1. Our results show that combining computational approach and experimental approach can effectively identify epitopes.

Cancer stem cell (CSC) theory has been proposed and verified in many cancers. The existence of osteosarcoma CSCs has been confirmed for many years and multiple surface markers have been employed to identify them. In this study, we identified CD271(+) subpopulation of osteosarcoma displaying stem-like properties. CD271, known as the neural crest nerve growth factor receptor, is the marker of bone marrow mesenchymal stem cells (MSCs) and human melanoma-initiating cells. We discovered that CD271 was expressed differentially in diverse types of human osteosarcoma and stabilized cell lines. CD271(+) osteosarcoma cells displayed most of the properties of CSC, such as self-renewal, differentiation, drug resistance and tumorigenicity in vivo. Nanog, Oct3/4, STAT3, DNA-PKcs, Bcl-2 and ABCG2 were more expressed in CD271(+) cells compared with CD271- cells. Our study supported the osteosarcoma CSC hypothesis and, to a certain extent, revealed one of the possible mechanisms involved in maintaining CSCs properties.