LOCATE: a mammalian protein subcellular localization database

LOCATE is a curated, web-accessible database that houses data describing the membrane organization and subcellular localization of mouse and human proteins. Over the past 2 years, the data in LOCATE have grown substantially. The database now contains high-quality localization data for 20% of the mouse proteome and general localization annotation for nearly 36% of the mouse proteome. The proteome annotated in LOCATE is from the RIKEN FANTOM Consortium Isoform Protein Sequence sets which contains 58 128 mouse and 64 637 human protein isoforms. Other additions include computational subcellular localization predictions, automated computational classification of experimental localization image data, prediction of protein sorting signals and third party submission of literature data. Collectively, this database provides localization proteome for individual subcellular compartments that will underpin future systematic investigations of these regions. It is available at http://locate.imb.uq.edu.au

Seasonal Variations in Mood and Behavior in Romanian Postgraduate Students

To our knowledge, this paper is the first to estimate seasonality of mood in a predominantly Caucasian sample, living in areas with hot summers and a relative unavailability of air conditioning. As a summer pattern of seasonal depression was previously associated with a vulnerability to heat exposure, we hypothesized that those with access to air conditioners would have a lower rate of summer seasonal affective disorder (SAD) compared to those without air conditioning. A convenience sample of 476 Romanian postgraduate students completed the Seasonal Pattern Assessment Questionnaire (SPAQ), which was used to calculate a global seasonality score (GSS) and to estimate the rates of winter- and summer-type SAD. The ratio of summer- vs. winter-type SAD was compared using multinomial probability distribution tests. We also compared the ratio of summer SAD in individuals with vs. without air conditioners. Winter SAD and winter subsyndromal SAD (S-SAD) were significantly more prevalent than summer SAD and summer S-SAD. Those with access to air conditioners had a higher, rather than a lower, rate of summer SAD. Our results are consistent with prior studies that reported a lower prevalence of summer than winter SAD in Caucasian populations. Finding an increased rate of summer SAD in the minority of those with access to air conditioners was surprising and deserves replication

Seasonal Changes in Sleep Duration in African American and African College Students Living In Washington, D.C.

Duration of nocturnal melatonin secretion, a marker of “biological night” that relates to sleep duration, is longer in winter than in summer in patients with seasonal affective disorder (SAD), but not in healthy controls. In this study of African and African American college students, we hypothesized that students who met criteria for winter SAD or subsyndromal SAD (S-SAD) would report sleeping longer in winter than in summer. In addition, based on our previous observation that Africans report more “problems” with change in seasons than African Americans, we expected that the seasonal changes in sleep duration would be greater in African students than in African American students. Based on Seasonal Pattern Assessment Questionnaire (SPAQ) responses, African American and African college students in Washington, D.C. (N = 575) were grouped into a winter SAD/S-SAD group or a no winter diagnosis group, and winter and summer sleep length were determined. We conducted a 2 (season) × 2 (sex) × 2 (ethnicity) × 2 (winter diagnosis group) ANCOVA on reported sleep duration, controlling for age. Contrary to our hypothesis, we found that African and African American students with winter SAD/S-SAD report sleeping longer in the summer than in the winter. No differences in seasonality of sleep were found between African and African American students. Students with winter SAD or S-SAD may need to sacrifice sleep duration in the winter, when their academic functioning/efficiency may be impaired by syndromal or subsyndromal depression, in order to meet seasonally increased academic demands

LOCATE: a mouse protein subcellular localization database

We present here LOCATE, a curated, web-accessible database that houses data describing the membrane organization and subcellular localization of proteins from the FANTOM3 Isoform Protein Sequence set. Membrane organization is predicted by the high-throughput, computational pipeline MemO. The subcellular locations of selected proteins from this set were determined by a high-throughput, immunofluorescence-based assay and by manually reviewing >1700 peer-reviewed publications. LOCATE represents the first effort to catalogue the experimentally verified subcellular location and membrane organization of mammalian proteins using a high-throughput approach and provides localization data for ∼40% of the mouse proteome. It is available at

Differential Use of Signal Peptides and Membrane Domains Is a Common Occurrence in the Protein Output of Transcriptional Units

Membrane organization describes the orientation of a protein with respect to the membrane and can be determined by the presence, or absence, and organization within the protein sequence of two features: endoplasmic reticulum signal peptides and alpha-helical transmembrane domains. These features allow protein sequences to be classified into one of five membrane organization categories: soluble intracellular proteins, soluble secreted proteins, type I membrane proteins, type II membrane proteins, and multi-spanning membrane proteins. Generation of protein isoforms with variable membrane organizations can change a protein's subcellular localization or association with the membrane. Application of MemO, a membrane organization annotation pipeline, to the FANTOM3 Isoform Protein Sequence mouse protein set revealed that within the 8,032 transcriptional units (TUs) with multiple protein isoforms, 573 had variation in their use of signal peptides, 1,527 had variation in their use of transmembrane domains, and 615 generated protein isoforms from distinct membrane organization classes. The mechanisms underlying these transcript variations were analyzed. While TUs were identified encoding all pairwise combinations of membrane organization categories, the most common was conversion of membrane proteins to soluble proteins. Observed within our high-confidence set were 156 TUs predicted to generate both extracellular soluble and membrane proteins, and 217 TUs generating both intracellular soluble and membrane proteins. The differential use of endoplasmic reticulum signal peptides and transmembrane domains is a common occurrence within the variable protein output of TUs. The generation of protein isoforms that are targeted to multiple subcellular locations represents a major functional consequence of transcript variation within the mouse transcriptome

Impact of the COVID-19 Pandemic on the Welfare of Animals in Australia

We report on the various responses in Australia during 2020 to minimize negative impacts of the COVID-19 pandemic on the welfare of animals. Most organizations and individuals with animals under their care had emergency preparedness plans in place for various scenarios; however, the restrictions on human movement to contain the spread of COVID-19, coupled with the economic impact and the health effects of COVID-19 on the skilled workforce, constituted a new threat to animal welfare for which there was no blueprint. The spontaneous formation of a national, multisectoral response group on animal welfare, consisting of more than 34 organizations with animals under their care, facilitated information flow during the crisis, which helped to mitigate some of the shocks to different organizations and to ensure continuity of care for animals during the pandemic. We conclude that animal welfare is a shared responsibility, and accordingly, a multisectoral approach to animal welfare during a crisis is required. Our experience demonstrates that to safeguard animal welfare during crises, nations should consider the following: a national risk assessment, clear communication channels, contingency plans for animal welfare, a crisis response group, and support systems for animal care providers. Our findings and recommendations from the Australian context may inform other countries to ensure that animal welfare is not compromised during the course of unpredictable events

Coronary artery height differences and their effect on fractional flow reserve

Background: Fractional flow reserve (FFR) uses pressure-based measurements to assess the severityof a coronary stenosis. Distal pressure (Pd) is often at a different vertical height to that of the proximalaortic pressure (Pa). The difference in pressure between Pd and Pa due to hydrostatic pressure, mayimpact FFR calculation.Methods: One hundred computed tomography coronary angiographies were used to measure heightdifferences between the coronary ostia and points in the coronary tree. Mean heights were used to calculate the hydrostatic pressure effect in each artery, using a correction factor of 0.8 mmHg/cm. Thiswas tested in a simulation of intermediate coronary stenosis to give the “corrected FFR” (cFFR) andpercentage of values, which crossed a threshold of 0.8.Results: The mean height from coronary ostium to distal left anterior descending (LAD) was +5.26 cm,distal circumflex (Cx) –3.35 cm, distal right coronary artery-posterior left ventricular artery (RCA-PLV)–5.74 cm and distal RCA-posterior descending artery (PDA) +1.83 cm. For LAD, correction resulted in a mean change in FFR of +0.042, –0.027 in the Cx, –0.046 in the PLV and +0.015 in the PDA. Using 200 random FFR values between 0.75 and 0.85, the resulting cFFR crossed the clinical treatmentthreshold of 0.8 in 43% of LAD, 27% of Cx, 47% of PLV and 15% of PDA cases.Conclusions: There are significant vertical height differences between the distal artery (Pd) and its point of normalization (Pa). This is likely to have a modest effect on FFR, and correcting for this results in a proportion of values crossing treatment thresholds. Operators should be mindful of this phenomenon when interpreting FFR values