Introduction

Academic Health Centers (AHCs) have complex, often competing missions. Many have developed mission-based management (MBM) systems to support their clinical and non-clinical missions. There are limited data on MBM use for their educational missions. Our scoping review explored how AHCs employed such systems.  Materials and methods: Arksey and O'Malley's six-stage framework guided our review. Based on pre-defined criteria, English language articles from PubMed, EMBASE, SCOPUS, and the Healthcare Administration Database published between 2010 and 2020 were loaded into a reference manager. The search included all health professions education schools. Articles were excluded if they were review articles, commentaries, or clearly did not involve funding for education. From the final list of selected articles, data were extracted using a data extraction sheet we developed. Two researchers reviewed each article again to ensure extracted data were reported consistently and with sufficient detail.  Results: Of the 1729 manuscripts identified, 35 met inclusion criteria. Sixteen (46%) contained data in some form but did not have a formal methods section describing the specific approach to data collection and analysis. Moreover, there was marked variability in how educational effort was quantified, what counted as educational effort (educational scholarship versus teaching) and the impacts of such quantification (departmental funding versus individual faculty incentives). None of the studies reported on the impact on faculty promotion. Faculty satisfaction with the system was reported in seven studies (20%) and was generally positive.

Conclusions

A systematic description of how systems were developed to support the educational mission was lacking. Clear goals, methods of development, uniform data on educational productivity and quality, and program evaluation were not defined by most articles. This lack of process clarity presents a challenge, but more importantly an opportunity for academic health centers to unify efforts and continue to further their educational mission.

A longstanding goal of biomedicine is to understand how alterations in molecular and cellular networks give rise to the spectrum of human diseases. For diseases with shared etiology, understanding the common causes allows for improved diagnosis of each disease, development of new therapies and more comprehensive identification of disease genes. Accordingly, this protocol describes how to evaluate the extent to which two diseases, each characterized by a set of mapped genes, are colocalized in a reference gene interaction network. This procedure uses network propagation to measure the network 'distance' between gene sets. For colocalized diseases, the network can be further analyzed to extract common gene communities at progressive granularities. In particular, we show how to: (1) obtain input gene sets and a reference gene interaction network; (2) identify common subnetworks of genes that encompass or are in close proximity to all gene sets; (3) use multiscale community detection to identify systems and pathways represented by each common subnetwork to generate a network colocalized systems map; (4) validate identified genes and systems using a mouse variant database; and (5) visualize and further investigate select genes, interactions and systems for relevance to phenotype(s) of interest. We demonstrate the utility of this approach by identifying shared biological mechanisms underlying autism and congenital heart disease. However, this protocol is general and can be applied to any gene sets attributed to diseases or other phenotypes with suspected joint association. A typical NetColoc run takes less than an hour. Software and documentation are available at https://github.com/ucsd-ccbb/NetColoc .

Providencia alcalifaciens is a Gram-negative bacterium found in various water and land environments and organisms, including insects and mammals. Some P. alcalifaciens strains encode gene homologs of virulence factors found in pathogenic Enterobacterales members, such as Salmonella enterica serovar Typhimurium and Shigella flexneri. Whether these genes are pathogenic determinants in P. alcalifaciens is not known. In this study, we investigated P. alcalifaciens-host interactions at the cellular level, focusing on the role of two type III secretion systems (T3SS) belonging to the Inv-Mxi/Spa family. T3SS1b is widespread in Providencia spp. and encoded on the chromosome. A large plasmid that is present in a subset of P. alcalifaciens strains, primarily isolated from diarrheal patients, encodes for T3SS1a. We show that P. alcalifaciens 205/92 is internalized into eukaryotic cells, lyses its internalization vacuole, and proliferates in the cytosol. This triggers caspase-4-dependent inflammasome responses in gut epithelial cells. The requirement for the T3SS1a in entry, vacuole lysis, and cytosolic proliferation is host cell type-specific, playing a more prominent role in intestinal epithelial cells than in macrophages or insect cells. In a bovine ligated intestinal loop model, P. alcalifaciens colonizes the intestinal mucosa and induces mild epithelial damage with negligible fluid accumulation in a T3SS1a- and T3SS1b-independent manner. However, T3SS1b was required for the rapid killing of Drosophila melanogaster. We propose that the acquisition of two T3SS has allowed P. alcalifaciens to diversify its host range, from a highly virulent pathogen of insects to an opportunistic gastrointestinal pathogen of animals.

A vexing observation in genome-wide association studies (GWASs) is that parallel analyses in different species may not identify orthologous genes. Here, we demonstrate that cross-species translation of GWASs can be greatly improved by an analysis of co-localization within molecular networks. Using body mass index (BMI) as an example, we show that the genes associated with BMI in humans lack significant agreement with those identified in rats. However, the networks interconnecting these genes show substantial overlap, highlighting common mechanisms including synaptic signaling, epigenetic modification, and hormonal regulation. Genetic perturbations within these networks cause abnormal BMI phenotypes in mice, too, supporting their broad conservation across mammals. Other mechanisms appear species specific, including carbohydrate biosynthesis (humans) and glycerolipid metabolism (rodents). Finally, network co-localization also identifies cross-species convergence for height/body length. This study advances a general paradigm for determining whether and how phenotypes measured in model species recapitulate human biology.