Detection and modeling of Staphylococcus aureus and fecal bacteria in Hawaiian coastal waters and sands.

Microbial pollution of recreational waters leads to millions of skin, respiratory, and gastrointestinal illnesses globally. Fecal indicator bacteria (FIB) are monitored to assess recreational waters but may not reflect the presence of Staphylococcus aureus, a global leader in bacterial fatalities. Since many community-acquired S. aureus skin infections are associated with high recreational water usage, this study measured and modeled S. aureus, methicillin-resistant S. aureus (MRSA), and FIB (Enterococcus spp., Clostridium perfringens) concentrations in seawater and sand at six beaches in Hilo, Hawai'i, USA, over 37 sample dates from July 2016 to February 2019 using culturing techniques. Generalized linear models predicted bacterial concentrations with physicochemical and environmental data. Beach visitors were also surveyed on their preferred activities. S. aureus and FIB concentrations were roughly 6-78 times higher at beaches with freshwater discharge than at those without. Seawater concentrations of Enterococcus spp. were positively associated with MRSA but not S. aureus. Elevated S. aureus was associated with lower tidal heights, higher freshwater discharge, onsite sewage disposal system density, and turbidity. Regular monitoring of beaches with freshwater input, utilizing real-time water quality measurements with robust modeling techniques, and raising awareness among recreational water users may mitigate exposure to S. aureus, MRSA, and FIB. PRACTITIONER POINTS: Staphylococcus aureus and fecal bacteria concentrations were higher in seawater and sand at beaches with freshwater discharge. In seawater, Enterococcus spp. positively correlated with MRSA, but not S. aureus. Freshwater discharge, OSDS density, water turbidity, and tides significantly predicted bacterial concentrations in seawater and sand. Predictive bacterial models based upon physicochemical and environmental data developed in this study are readily available for user-friendly application.

Neonatal apneic phenotype in a murine congenital central hypoventilation syndrome model is induced through non-cell autonomous developmental mechanisms.

Congenital central hypoventilation syndrome (CCHS) represents a rare genetic disorder usually caused by mutations in the homeodomain transcription factor PHOX2B. Some CCHS patients suffer mainly from deficiencies in CO2 and/or O2 respiratory chemoreflex, whereas other patients present with full apnea shortly after birth. Our goal was to identify the neuropathological mechanisms of apneic presentations in CCHS. In the developing murine neuroepithelium, Phox2b is expressed in three discrete progenitor domains across the dorsal-ventral axis, with different domains responsible for producing unique autonomic or visceral motor neurons. Restricting the expression of mutant Phox2b to the ventral visceral motor neuron domain induces marked newborn apnea together with a significant loss of visceral motor neurons, RTN ablation, and preBötzinger complex dysfunction. This finding suggests that the observed apnea develops through non-cell autonomous developmental mechanisms. Mutant Phox2b expression in dorsal rhombencephalic neurons did not generate significant respiratory dysfunction, but did result in subtle metabolic thermoregulatory deficiencies. We confirm the expression of a novel murine Phox2b splice variant which shares exons 1 and 2 with the more widely studied Phox2b splice variant, but which differs in exon 3 where most CCHS mutations occur. We also show that mutant Phox2b expression in the visceral motor neuron progenitor domain increases cell proliferation at the expense of visceral motor neuron development. We propose that visceral motor neurons may function as organizers of brainstem respiratory neuron development, and that disruptions in their development result in secondary/non-cell autonomous maldevelopment of key brainstem respiratory neurons.

Machine learning-based data analytic approaches for evaluating post-natal mouse respiratory physiological evolution.

Respiratory parameters change during post-natal development, but the nature of their changes have not been well-described. The advent of commercially available plethysmographic instruments provided improved repeatability of measurements and standardization of measured breathing in mice across laboratories. These technologies thus allowed for exploration of more precise respiratory pattern changes during the post-natal developmental epoch. Current methods to analyze respiratory behavior utilize plethysmography to acquire standing values of frequency, volume and flow at specific time points in murine maturation. These metrics have historically been independently analyzed as a function of time with no further analysis examining the interplay these variables have with each other and in the context of postnatal maturation or during blood gas homeostasis. We posit that machine learning workflows can provide deeper physiological understanding into the postnatal development of respiration. In this manuscript, we delineate a machine learning workflow based on the R-statistical programming language to examine how variation and relationships of frequency (f) and tidal volume (TV) change with respect to inspiratory and expiratory parameters. Our analytical workflows could successfully predict age and found that the variation and relationships between respiratory metrics are dynamically shifting with age and during hypercapnic breathing. Thus, our work demonstrates the utility of high dimensional analyses to provide reliable class label predictions using non-invasive respiratory metrics. These approaches may be useful in large-scale phenotyping across development and in disease.

Differentiating Staphylococcus infection-associated glomerulonephritis and primary IgA nephropathy: a mass spectrometry-based exploratory study.

Staphylococcus infection-associated glomerulonephritis (SAGN) and primary IgA nephropathy (IgAN) are separate disease entities requiring different treatment approaches. However, overlapping histologic features may cause a diagnostic dilemma. An exploratory proteomic study to identify potential distinguishing biomarkers was performed on formalin fixed paraffin embedded kidney biopsy tissue, using mass spectrometry (HPLC-MS/MS) (n = 27) and immunohistochemistry (IHC) (n = 64), on four main diagnostic groups-SAGN, primary IgAN, acute tubular necrosis (ATN) and normal kidney (baseline transplant biopsies). Spectral counts modeled as a negative binomial distribution were used for statistical comparisons and in silico pathway analysis. Analysis of variance techniques were used to compare groups and the ROC curve to evaluate classification algorithms. The glomerular proteomes of SAGN and IgAN showed remarkable similarities, except for significantly higher levels of monocyte/macrophage proteins in SAGN-mainly lysozyme and S100A9. This finding was confirmed by IHC. In contrast, the tubulointerstitial proteomes were markedly different in IgAN and SAGN, with a lower abundance of metabolic pathway proteins and a higher abundance of extracellular matrix proteins in SAGN. The stress protein transglutaminase-2 (TGM2) was also significantly higher in SAGN. IHC of differentially-expressed glomerular and tubulointerstitial proteins can be used to help discriminate between SAGN and IgAN in ambiguous cases.

Development of a Novel FIJI-Based Method to Investigate Neuronal Circuitry in Neonatal Mice.

The emergence of systems neuroscience tools requires parallel generation of objective analytical workflows for experimental neuropathology. We developed an objective analytical workflow that we used to determine how specific autonomic neural lineages change during postnatal development. While a wealth of knowledge exists regarding postnatal alterations in respiratory neural function, how these neural circuits change and develop in the weeks following birth remains less clear. In this study, we developed our workflow by combining genetic mouse modeling and quantitative immunofluorescent confocal microscopy and used this to examine the postnatal development of neural circuits derived from the transcription factors NKX2.2 and OLIG3 into three medullary respiratory nuclei. Our automated FIJI-based image analysis workflow rapidly and objectively quantified synaptic puncta in user-defined anatomic regions. Using our objective workflow, we found that the density and estimated total number of Nkx2.2-derived afferents into the pre-Bötzinger Complex significantly decreased with postnatal age during the first three weeks of postnatal life. These data indicate that Nkx2.2-derived structures differentially influence pre-Bötzinger Complex respiratory oscillations at different stages of postnatal development.