In the human stomach, the oxyntic epithelium includes numerous tubular invaginations consisting of short pits opening into long glands. The pit is lined by pit cells, whereas the gland is composed of three regions: the base, containing zymogenic cells; the neck, containing neck cells; and the isthmus, composed of little known immature cells and of parietal cells, which are also scattered through the neck and base. The aim of this study was to examine the ultrastructure of the immature cells and to determine their relation to mature cells. To do so, normal oxyntic mucosal biopsies from subjects ranging from 20-43 years old were fixed in aldehydes and postfixed in reduced osmium for electron microscopy and morphometric analysis. The immature cells were sorted out into four classes, whose roles were clarified by comparison with the thoroughly investigated mouse oxyntic epithelium. The first class was composed of the least differentiated immature cells, which were rare and characterized by minute, dense, or cored secretory granules and were accordingly named mini-granule cells. Their function was not clarified. The second class consisted of pre-pit cells, which were characterized by few dense mucous granules and give rise to pit cells that ascend the pit wall and, after reaching the luminal surface, die or are extruded. Both pre-pit and pit cells underwent continuous renewal and, therefore, together constituted a renewal system referred to as pit cell lineage. The third class, or pre-neck cells, characterized by cored secretory granules, give rise to neck cells that descend toward the base region and differentiate further into pre-zymogenic cells, which finally become zymogenic cells. The latter eventually degenerate and die. Thus pre-neck cells and their progeny constitute a renewing system, designated zymogenic cell lineage. The fourth class, or pre-parietal cells, characterized by long microvilli and few tubulovesicles, differentiate into parietal cells that descend along the neck and base regions and eventually degenerate and die. Pre-parietal and parietal cells represent a renewing system referred to as parietal cell lineage. While the origin of the last three classes of progenitor cells has not been elucidated, it is likely that they arise either from an unidentified multipotential stem cell, possibly the mini-granule cell itself, or from the mitotic activity of pre-pit and pre-neck cells. In conclusion, the human oxyntic epithelium is composed of continually renewing cells organized in distinct cell lineages.

Acute SIV infection is characterized by explosive infection of memory CD4 T cells in peripheral and mucosal tissues. Interestingly, relatively few memory CD4 T cells are infected until as late as days 7–8 after challenge. However, by day 10 postinfection, most of the memory CD4 T cells are infected and carry viral DNA. The rapidity with which infection expands within 2–3 days to encompass virtually the entire memory CD4 T cell compartment suggests significant alterations in the susceptibility of memory CD4 T cells to infection during this period. The mechanism(s) underlying this increased permissiveness to infection is not known. In this study, we show that IL-15 secretion significantly correlates with the up-regulated expression of CD4 on memory CD4 T cells that is associated with increased permissiveness to SIV infection. Activation and proliferation of memory CD8, but not memory CD4 T cells, preceded the amplification of viral infection. Although memory CD4 T cells did not express normal activation markers, they displayed a significant up-regulation in the density of CD4 but not CCR5 expression between days 7 and 10 postinfection that correlated with increased plasma IL-15 levels and infection in these cells. Culture of purified CD4 T cells with IL-15 and/or SIV was associated with a significant increase in the expression of CD4 and infection of these sorted cells. Our results demonstrate that IL-15 contributes to the increased susceptibility of memory CD4 T cells to SIV during the early phase of acute SIV infection.

Multiple neurodegenerative diseases are causally linked to aggregation-prone proteins. Cellular mechanisms involving protein turnover may be key defense mechanisms against aggregating protein disorders. We have used a transgenic Caenorhabditis elegans Alzheimer's disease model to identify cellular responses to proteotoxicity resulting from expression of the human beta amyloid peptide (Aβ). We show up-regulation of aip-1 in Aβ-expressing animals. Mammalian homologues of AIP-1 have been shown to associate with, and regulate the function of, the 26S proteasome, leading us to hypothesize that induction of AIP-1 may be a protective cellular response directed toward modulating proteasomal function in response to toxic protein aggregation. Using our transgenic model, we show that overexpression of AIP-1 protected against, while RNAi knockdown of AIP-1 exacerbated, Aβ toxicity. AIP-1 overexpression also reduced accumulation of Aβ in this model, which is consistent with AIP-1 enhancing protein degradation. Transgenic expression of one of the two human aip-1 homologues (AIRAPL), but not the other (AIRAP), suppressed Aβ toxicity in C. elegans, which advocates the biological relevance of the data to human biology. Interestingly, AIRAPL and AIP-1 contain a predicted farnesylation site, which is absent from AIRAP. This farnesylation site was shown by others to be essential for an AIP-1 prolongevity function. Consistent with this, we show that an AIP-1 mutant lacking the predicted farnesylation site failed to protect against Aβ toxicity. Our results implicate AIP-1 in the regulation of protein turnover and protection against Aβ toxicity and point at AIRAPL as the functional mammalian homologue of AIP-1.

Autism is a neurodevelopmental disorder that displays significant heterogeneity. Comparison of subgroups within autism, and analyses of selected biomarkers as measure of the variation of the severity of autistic features such as cognitive dysfunction, social interaction impairment, and sensory abnormalities might help in understanding the pathophysiology of autism. In this study, two sets of biomarkers were selected. The first included 7, while the second included 6 biomarkers. For set 1, data were collected from 35 autistic and 38 healthy control participants, while for set 2, data were collected from 29 out of the same 35 autistic and 16 additional healthy subjects. These markers were subjected to a principal components analysis using either covariance or correlation matrices. Moreover, libraries composed of participants categorized into units were constructed. The biomarkers used include, PE (phosphatidyl ethanolamine), PS (phosphatidyl serine), PC (phosphatidyl choline), MAP2K1 (Dual specificity mitogen-activated protein kinase kinase 1), IL-10 (interleukin-10), IL-12, NFκB (nuclear factor-κappa B); PGE2 (prostaglandin E2), PGE2-EP2, mPGES-1 (microsomal prostaglandin synthase E-1), cPLA2 (cytosolic phospholipase A2), 8-isoprostane, and COX-2 (cyclo-oxygenase-2). While none of the studied markers correlated with CARS and SRS as measure of cognitive and social impairments, six markers significantly correlated with sensory profiles of autistic patients. Multiple regression analysis identifies a combination of PGES, mPGES-1, and PE as best predictors of the degree of sensory profile impairment. Library identification resulted in 100% correct assignments of both autistic and control participants based on either set 1 or 2 biomarkers together with a satisfactory rate of assignments in case of sensory profile impairment using different sets of biomarkers. The two selected sets of biomarkers were effective to separate autistic from healthy control subjects, demonstarting the possibility to accurately predict the severity of autism using the selected biomarkers. The effectiveness of the identified libraries lied in the fact that they were helpful in correctly assigning the study population as control or autistic patients and in classifying autistic patients with different degree of sensory profile impairment.

The goal of the study was to determine which similarity coefficient and statistical method to use to produce the highest rate of correct assignment (RCA) in repetitive extragenic palindromic PCR-based bacterial source tracking. In addition, the use of standards for deciding whether to accept or reject source assignments was investigated. The use of curve-based coefficients Cosine Coefficient and Pearson's Product Moment Correlation yielded higher RCAs than the use of band-based coefficients Jaccard, Dice, Jeffrey's x, and Ochiai. When enterococcal and Escherichia coli isolates from known sources were used in a blind test, the use of maximum similarity produced consistently higher RCAs than the use of average similarity. We also found that the use of a similarity value threshold and/or a quality factor threshold (the ratio of the average fingerprint similarity within a source to the average similarity of this source's isolates to an unknown) to decide whether to accept source assignments of unknowns increases the reliability of source assignments. Applying a similarity value threshold improved the overall RCA (ORCA) by 15 to 27% when enterococcal fingerprints were used and 8 to 29% when E. coli fingerprints were used. Applying the quality factor threshold resulted in a 22 to 32% improvement in the ORCA, depending on the fingerprinting technique used. This increase in reliability was, however, achieved at the expense of decreased numbers of isolates that were assigned a source.