Identification of the Bartonella autotransporter CFA as a protective antigen and hypervariable target of neutralizing antibodies in mice.

The bacterial genus Bartonella comprises numerous emerging pathogens that cause a broad spectrum of disease manifestations in humans. The targets and mechanisms of the anti-Bartonella immune defense are ill-defined and bacterial immune evasion strategies remain elusive. We found that experimentally infected mice resolved Bartonella infection by mounting antibody responses that neutralized the bacteria, preventing their attachment to erythrocytes and suppressing bacteremia independent of complement or Fc receptors. Bartonella-neutralizing antibody responses were rapidly induced and depended on CD40 signaling but not on affinity maturation. We cloned neutralizing monoclonal antibodies (mAbs) and by mass spectrometry identified the bacterial autotransporter CFA (CAMP-like factor autotransporter) as a neutralizing antibody target. Vaccination against CFA suppressed Bartonella bacteremia, validating CFA as a protective antigen. We mapped Bartonella-neutralizing mAb binding to a domain in CFA that we found is hypervariable in both human and mouse pathogenic strains, indicating mutational antibody evasion at the Bartonella subspecies level. These insights into Bartonella immunity and immune evasion provide a conceptual framework for vaccine development, identifying important challenges in this endeavor.

Malaria parasite detection and cell counting for human and mouse using thin blood smear microscopy.

Despite the remarkable progress that has been made to reduce global malaria mortality by 29% in the past 5 years, malaria is still a serious global health problem. Inadequate diagnostics is one of the major obstacles in fighting the disease. An automated system for malaria diagnosis can help to make malaria screening faster and more reliable. We present an automated system to detect and segment red blood cells (RBCs) and identify infected cells in Wright-Giemsa stained thin blood smears. Specifically, using image analysis and machine learning techniques, we process digital images of thin blood smears to determine the parasitemia in each smear. We use a cell extraction method to segment RBCs, in particular overlapping cells. We show that a combination of RGB color and texture features outperforms other features. We evaluate our method on microscopic blood smear images from human and mouse and show that it outperforms other techniques. For human cells, we measure an absolute error of 1.18% between the true and the automatic parasite counts. For mouse cells, our automatic counts correlate well with expert and flow cytometry counts. This makes our system the first one to work for both human and mouse.