Measurements were made at 5-minute intervals over consecutive days for nearly one year, providing a total of ∼8×106 data points. Indoor temperature, illuminance, and human occupancy/activity were all weakly correlated between rooms, while relative humidity, humidity ratio, and outdoor air fractions showed strong temporal  patterns and strong spatial correlations between rooms. Differential pressure measurements confirmed that all patient rooms were operated at neutral pressure. The patient rooms averaged about 100 combined entrances and exits per day, which suggests they were relatively lightly occupied compared to higher traffic environments  and more similar to lower traffic office environments. There were also clear differences in several environmental parameters before and after the hospital was occupied with patients and staff. Characterizing and understanding factors that influence these building dynamics is vital for hospital environments, where they can impact patient health and the survival and spread of healthcare associated infections.The dynamics of indoor environmental conditions, human occupancy, and operational characteristics of buildings influence human comfort and indoor environmental quality, including the survival and progression of microbial communities. A suite of continuous, long-term environmental and operational parameters were measured in ten patient rooms and two nurse stations in a new hospital building in Chicago, IL to characterize the indoor environment in which microbial samples were taken for the Hospital Microbiome Project. Measurements included environmental conditions  in the patient rooms and nurse stations; differential pressure between the patient rooms and hallways; surrogate measures for human occupancy and activity in the patient rooms using both indoor air CO The measurement campaign occurred immediately before the hospital was occupied (for approximately one month) and after the introduction of patients and hospital staff (for nearly 11 months). While samples were collected for culture-independent sequence-based analysis of bacterial and fungal communities , a large dataset of building environmental and operational parameters, or built environment data, were also collected. This built environment dataset provides an extensive characterization of the indoor parameters that may have influenced human comfort, indoor environmental quality, and the survival and succession of microbial communities within the hospital.Certain building design, environmental, and operational factors are important to characterize in critical building environments, such as hospitals, because they influence occupant comfort and health –5, indooThis work details the field equipment, methodology, and spatial and temporal variations in the built environment data to determine whether there were significant differences in the range of built environment parameters within individual rooms, between different rooms, and between floors. Importantly, this study enables us to examine the impact of time of day, season, and the introduction of human occupants on these built environment parameters to determine the potential implications of their variance on human comfort and indoor environmental quality. Additionally, the potential influence of the built environment parameters on the survival and spread of microbial communities can be hypothesized; however comparisons between the actual built environment and microbial datasets will be made once all microbial samples have been analyzed.The following sections describe the measurement sites and the built environment measurement and analysis methodology in detail.th and 10th floors). The 10 patient rooms were nearly identical, single-occupancy, west-facing perimeter adult inpatient rooms, classified as neutral pressure rooms on the mechanical plans. Their floor plans were flipped such that their sinks and bathrooms were installed along shared walls. While all 10 rooms were patient care units, the 10th floor rooms were devoted to oncology patients who typically had longer stays than patients on the 9th floor. Each room had a floor area of approximately 33 m2, including a bathroom of 4.6 m2. Ceiling heights were 2.9 m, providing a room volume of approximately 97 m3. Large windows spanned the width of the west wall of each room, opposite the sole doorway on the east side. Two 1.5 m slot supply air diffusers were located at the ceiling near the windows, spanning the width of the room. The design supply airflow rate was 765 m3/hr according to detail drawings and schedule sheets, with minimum flow rates specified as 663 m3/hr. The heating, ventilating, and air-conditioning (HVAC) systems conditioned air centrally in shared air handling units (AHUs) and reheat coils at the supply diffusers provided occupant temperature control within individual rooms. A single 0.6 m × 0.6 m return grille was located at the ceiling near the doorway with a design airflow rate of 595 m3/hr and a minimum airflow rate of 493 m3/hr, according to the schedule sheets. An additional exhaust grille was located in the bathroom with a constant design airflow rate of 170 m3/hr.Five patient rooms and one nurse station were chosen for sampling on each of two floors  that also served the 8th floor. These four AHUs were connected at a common return plenum (or mixing box) where recirculated air was mixed with outdoor air, conditioned, filtered, and delivered into a common supply plenum that then split into four individual supply air plenums for distribution via ductwork throughout the hospital. Each AHU had MERV 7 and MERV 13 pre-filters installed before the heating and cooling coils and supply fans, as well as HEPA filtration installed just before the entrance to the supply plenum. Dampers at the outdoor air intakes were automated for economizer control, which adjusted the outdoor air intake flow rate depending primarily on the outdoor air temperature.Patient rooms and nurse stations on each floor were served by AHUs located in the two-level mechanical penthouse on the 11th and 10th floors of the hospital. Room numbers 101–105 refer to consecutive rooms on the 9th floor, with 100 referring to the nurse station on the same floor. Room numbers 201–205 refer to the consecutive rooms on the 10th floor, directly above those on the 9th floor, with 200 referring to the nurse station on the 10th floor. AHU 6 refers to the single air handling unit that served the 10th floor. AHU 11 refers to the combination of four air handling units that combined to serve the 8th and 9th floors.In order to de-identify patient rooms and nurse stations for purposes of confidentiality, a naming convention was created to refer to rooms on the 9Indoor environmental conditions, including air dry-bulb temperature, relative humidity, humidity ratio (a measure of absolute humidity or the moisture content of air), and illuminance (a measure of incident light) in the 10 patient rooms and two nurse stations.Differential pressure between the 10 patient rooms and the hallways.2 concentrations and infrared (IR) beam-break counters installed at the patient room doorways.Surrogate measures of human occupancy and activity in the 10 patient rooms using both indoor COOutdoor air fractions in the HVAC systems serving the two floors.The built environment data collection campaign and corresponding analysis included long-term characterizations of:Many of these parameters are suggested in both the MIxS-BE package for minimal metadata collection  and in aFig. 1. In the two nurse stations, the temperature, relative humidity, and light sensors were mounted vertically on a counter in a central area at approximately the same 1.4 m height . The 5-minute measurement interval of these sensors was synchronized with all other Onset HOBO data loggers throughout the hospital. Periodic surface temperature measurements were also made with an IR thermometer on several sampling sites early in the study but were discontinued because they correlated very strongly to indoor air temperatures at the time of measurement.Onset HOBO U12-012 data loggers were used to record temperature (±0.4°C at 25°C), relative humidity (±2.5% from 10% to 90% RH), and illuminance (uncertainty unknown). In the patient rooms, the data loggers were installed at a height of approximately 1.4 m on the wall adjacent to the patient bed and across from the large windows. This location was chosen because much of the microbial sampling would occur at or around the beds and the data loggers would capture the illuminance representative of the majority of the sampling space. The location of each sensor in the patient rooms is shown in Onset T-VER-PXU-X differential pressure transducers  were installed at each patient room doorway and connected to a large battery pack and an Onset HOBO U12 data logger (±2 mV and ± 2.5% of absolute reading) with tubing placed on each side of the doorway to measure the pressure differential between the patient rooms and the adjacent hallway. This measurement was designed to serve as an indicator of whether the rooms were being operated at neutral pressure, positive pressure isolation , or negative pressure isolation . The pressure sensors required 12 VDC power supply and drew approximately 35 mA when operating. Therefore, the pressure sensors were connected to battery packs with 8 D batteries and housed together in a nondescript black plastic project enclosure box. This box was mounted on the wall with adhesive strips. Clear vinyl tubing was used to measure the pressure differential and data were logged to an Onset HOBO data logger at 5-minute intervals, synchronized with other HOBO loggers throughout the hospital. Field measurements using an Energy Conservatory DG-700 differential pressure sensor confirmed very low, typically neutral pressures with respect to the hallway, so the pressure transducers were set to bi-directional operation with a maximum range of ±25 Pa.activity through the doorway of each room, or the combined number of entrances and exits over each 5-minute period, but not necessarily the actual time-varying occupancy. However a reasonable estimate of the daily total occupancy in each room could be determined by summing the number of beam-breaks over a 24-hour period and assuming that half of the beam-breaks were entrances and the other half were exits.Patient room occupancy and activity were assessed using two surrogate measurement methods. One was the installation of SenSource PC-TB12-R non-directional, single IR beam-break people counters mounted at a height of approximately 0.6 m at each doorframe to detect the combined number of entrances and exits through the patient room doorways over 5-minute intervals . Dual-direction beam-break counters and other methods exist, but exceeded the budget for this project and/or would violate patient confidentiality concerns. The beam-break measurements in this investigation were most helpful for inferring the level of 2 concentrations were also measured in each patient room using PP Systems SBA-5 CO2 gas analyzers . The CO2 concentrations measured in each room were later combined with CO2 concentrations measured concurrently in the supply airstream of the HVAC system serving each floor (described in the next section) in order to provide a metric of “room-source” CO2 (Croom-source = Croom—Csupply), or the CO2 concentration that results from occupant generation alone, adjusted for varying ventilation rates. These analyzers were installed in another nondescript black plastic project box on a small shelf within the rooms, with clear vinyl tubing running out to the sampled area (at approximately counter height near the edge of the sink). This location was chosen primarily because of easy access to electrical power, since the analyzers required 12 VDC power supplies and could not operate for long durations on batteries. Data from the CO2 analyzers were output to another Onset HOBO data logger (±2 mV and ±2.5% of absolute reading) attached to the side of the project box to allow for easy retrieval and for synchronizing with all other HOBO data loggers.To support the IR beam-break measurements, indoor air CO2 concentrations were measured in each of the outdoor, return, and supply air streams at each of the two AHUs in the mechanical room using the same type of CO2 analyzers as in the patient rooms (PP Systems SBA-5) connected to Onset HOBO U12 data loggers. The fraction of outdoor air delivered to each floor was calculated using a combination of CO2 data from all three air streams from each AHU [2 sensors in each air stream in quadrature [To assess outdoor air fractions in the HVAC systems serving the sample floors, COeach AHU . Uncertaadrature .Air temperature was also measured in the outdoor, return, and supply air streams using an Onset TMC20-HD temperature sensor connected to an Onset HOBO U12 data logger (±0.25°C from 0°C to 50°C). Relative humidity was measured in both return air streams with an Onset HOBO U12 data logger. Water vapor pressure was measured directly in the two supply air streams and in one outdoor air stream (AHU 6) using the SBA-5 monitors and compared to the saturation vapor pressure at each measured temperature to calculate relative humidity in these locations. These measurements were also synchronized with the patient room and nurse station measurements and logged at 5-minute intervals. The temperature and humidity measurements from each air stream also served to validate estimates of outdoor air fractions with data from the hospital facilities department, who occasionally provided information on air handling unit damper positions over select 24-hour periods. These data were used to calibrate outdoor air fraction estimates using a procedure described in the Supporting Information (SI).2 sensors were deployed during a short field campaign in February 2013  to assess mixing characteristics in one of the patient rooms; results confirmed that the rooms were reasonably well-mixed due in large part to very high airflow rates relative to indoor volumes. The flow measurements are described in more detail in Table A in S1 File, along with methods for calibration and co-location of all relevant sensors .Finally, only spot measurements of the airflow rates in individual patient rooms were made prior to the hospital opening due to the level of invasiveness required for these measurements. Both recirculated airflow rates and exhaust airflow rates were measured using a pressure-matching technique combined with a calibrated fan (Energy Conservatory Duct Blaster). Similar measurements were also performed at the two slot supply diffusers; although we have much more confidence in the return and exhaust flow measurements because of the simpler geometries involved. Additionally, several COUpon initial sensor deployment, data were collected on a weekly basis. All collected built environment data was managed and analyzed using the data analysis and statistical software package Stata Version 13. Data recorded within 15 minutes of the weekly data collection period for each sensor and logger combination were excluded to account for the time it takes to disconnect and reconnect data loggers. The resulting dataset was then explored to address the research questions described herein.6 data points over the course of the project (Table 1). The hospital was relatively tightly controlled for thermal comfort, with the majority  of patient room and nurse station air temperatures falling within a 2.1°C range. Room air relative humidity and humidity ratio showed greater variation than temperature, but were still relatively tightly controlled; the IQR for relative humidity and humidity ratio spanned ∼10% and ∼1.6 gw/kgda, respectively . The wide range of illuminance values  accounts for minimal light during nighttime hours and much higher levels during daytime hours with high solar insolation penetrating the glazing. Values for both room air CO2 concentrations and IR beam-break counts varied widely during the 5-minute measurement intervals, with the IQR spanning a range of 53 ppm and 1 count, respectively . Finally, differential pressure measurements revealed that the rooms were indeed neutral pressure environments, with a mean near 0 Pa and an IQR spanning only 0.9 Pa .The patient room and nurse station measurements produced approximately ∼7.26 x106 data points made in the mechanical room, including CO2 concentrations, air temperature, relative humidity, and humidity ratio in the three airstreams (Table 2). Outdoor air temperatures ranged from −14.6°C to +31.0°C across the year. Return air and supply air temperatures were much more consistent with median values (and IQR ranges) of 23.1°C (0.4°C) and 12.1°C (0.7°C), respectively. Outdoor relative humidity and humidity ratio varied widely in the Chicago climate, while return and supply air relative humidity and humidity ratio reflected a combination of influences from outdoor air fractions and cooling operation. Outdoor air and supply air CO2 concentrations were consistently lower than return air CO2 concentrations, suggesting that the HVAC systems often operated closer to 100% outdoor air than 100% return air . Supply air CO2 concentrations were often lower than outdoor air CO2 concentrations; however this should not be the case and this anomaly is likely explained by instrument uncertainty, as the differences between supply air and outdoor air CO2 concentrations were almost always within propagated instrument uncertainty. Therefore, outdoor air fractions were difficult to estimate accurately given the very small concentration differences observed. However, we explore an alternate estimation procedure for outdoor air fractions and also compare these estimates to damper information from the building facilities department in the SI .The HVAC system measurements produced an additional ∼1.64 x10Fig. 2A). Hourly average air temperatures across all patient rooms typically varied between approximately 19°C and 26°C (a range of about 7°C), with median hourly temperatures ranging between 22°C and 25°C . The observed variation likely stems from patient comfort preferences, as each patient had the ability to control their room air temperature. Nurse stations appeared to be more centrally controlled, with hourly average air temperatures spanning a range of only about 4°C. Across all rooms, there was no correlation between hourly average indoor air temperatures and outdoor air temperatures , suggesting that outdoor temperatures had no influence on indoor temperatures. Slight differences in indoor air temperatures between the lower floor and upper floor were apparent in both patient rooms and nurse stations, with median temperatures about 1°C higher on the upper floor, suggesting an influence of the HVAC systems or building design. One point of distinction occurred in Room 101, in that several hourly data points were greater than 30°C ; althouFig. 2B). Hourly average relative humidity values  in both patient rooms and nurse stations demonstrated greater variation, from 10% to 60%; although lower values (<25%) occurred quite infrequently. The majority  of hourly average relative humidity measurements were between 25% and 50%, with median relative humidity values around 35% and values fairly consistent across all rooms and nurse stations. It is important to note the similarities in variation within patient rooms and nurse stations, indicating the strong effect from the hospital HVAC systems and little effect from individual microenvironments. Hourly relative humidity distributions typically consisted of two primary modes in most locations, albeit with some scatter. Daily averages of relative humidity were also significantly greater after the hospital opened , although the differences were largely driven by seasonal variations. For example, there were only minor, non-significant differences in daily mean relative humidity values in the patient rooms before or after opening if data were restricted only to days in January of both 2013 and 2014 (the only shared month).Weekly average relative humidity values ranged from ∼15% to 48% across all patient rooms and nurse stations . Variations in both weekly and hourly average humidity ratios within rooms and nurse stations appeared consistent and similar across all measurement locations, with the majority of measurements falling between 5 gw/kgda and 8 gw/kgda . However, there was noticeably less variation in humidity ratios (and to a lesser extent relative humidity) from weeks 16–28 (which occurred in the summer months of Jun—Sep 2013). In fact, there was no correlation between hourly average indoor humidity ratios across all rooms and nurse stations and outdoor humidity ratios between ∼8 and 14 gw/kgda, while there was a strong linear correlation between indoor and outdoor humidity ratios between 2 and 8 gw/kgda . There was also a clear difference between floors, with indoor humidity ratios on the upper floor consistently greater than the lower floor . Hourly humidity ratio distributions consisted of two very clear modes in all locations, with peaks around 5.6 and 7.1 gw/kgda on the lower floor and around 6.1 and 7.2 gw/kgda on the upper floor. These data suggest that the influence of the HVAC system on indoor humidity ratio considerably overwhelmed influences from individual patient activities or thermostat control.The humidity ratio at each interval was calculated using concurrent temperature and relative humidity data from patient rooms and nurse stations . Weekly Fig. 2D). Distributions of illuminance levels in the patient rooms and nurse stations were summarized using daily averages instead of hourly averages (hourly variations were quite extreme due to large differences between daytime and nighttime). Daily average illuminance levels ranged from 0 to 1400 lx across all sampling locations, but the majority of daily averages were less than 200 lx . Median values were similar across all locations as well, although all patient rooms  had several days with much higher illuminance. Daily mean illuminance levels at the nurse stations  clustered between about 100 and 200 lx, suggesting that the large values observed in patient rooms potentially resulted from periods of high solar radiation. Indeed, illuminance levels in patient rooms increased most during the afternoon hours, with increasing solar radiation during this time of day on the west-facing facades . Additionally, nurse station lights were dimmed rather than shut off during evening hours, so nurse station illuminance levels were never as low as observed for patient rooms.Weekly average illuminance levels ranged from approximately 10 lx to 590 lx across all patient rooms and from approximately 75 lx to 190 lx at the nurse stations , with median daily total counts between 175 and 225 for all rooms. Assuming that half of the beam-breaks over the course of a day represent entrances and half represent exits, the median number of combined entrances and exists was typically between 90 and 110 in the patient rooms . Over all rooms, few days had less than 100 beam-breaks , indicating that all patient rooms typically averaged at least 4 beam-breaks per hour , on average.Daily total beam-break counts demonstrated similar variance across all patient rooms . This was largely driven by a small number of high occupancy periods during a few days in July and October 2013, although the cause of this elevated occupancy/activity cannot be explained by our data. Additionally, the introduction of patients and staff clearly influenced the beam-break occupancy surrogates as expected. There were typically 2.5–3 times as many daily total beam-breaks in each room after the hospital opened in February 2013 .2 concentrations were relatively similar among rooms, ranging between ∼40 and ∼61 ppm . However, propagated instrument uncertainty was as high as 28 ppm for each sensor. This maximum value is determined by adding the CO2 sensor uncertainty reported by the manufacturer (20 ppm) and data logger uncertainty (2 mV ± 2.5% absolute reading) in quadrature . Using that maximum value, the propagated uncertainty in Croom-source was estimated to be approximately 40 ppm by adding the compounded uncertainties for individual sensors used in pair-wise comparisons in quadrature. However, the use of the same CO2 sensors in each patient room throughout the project provides greater confidence in these data , which suggest that distributions of room-source CO2 concentrations were wider in some rooms and implies that occupancy patterns demonstrated greater variation in those rooms .Median values of daily average room-source CO2 concentrations and daily total IR beam-break counts . The two surrogate measures in each patient room were reasonably well correlated with statistically significant relationships in all comparisons and correlation coefficients ranging from 0.29 to 0.66. These data suggest that both sensors may be able to serve as reasonable indicators of human occupancy in the patient rooms. In theory, the room-source CO2 measurements could provide accurate estimates of occupancy using a mass balance approach; however, this method suffers from large uncertainty and a number of instrumentation reliability issues . Conversely, the IR beam-break sensors proved to be more robust and seldom had missing data; however, daily beam-break totals are not a direct measure of average occupancy, and are a better surrogate for overall occupant activity.We also explored correlations between average daily occupancy based on room-source COR < 0.2, with the exception of a few room combinations (S1 File). However, there were no significant correlations if comparisons are limited only to measurements made after the hospital was occupied, suggesting that any significant correlations were driven by those that occurred prior to the hospital opening when there were no occupants controlling temperature for comfort. Overall, ∼28.7% of all measured differences in daily average air temperatures were found to be within the range of propagated instrument uncertainty  in quadrature), with 18.1% to 42.9% of measurement differences falling within this range of uncertainty for individual room comparisons (Table D in S1 File). Additionally, we have good confidence that the actual uncertainty in the temperature measurements is lower according to our co-location calibration experiments. Therefore, the weak correlation observed is believed to largely stem from true differences in daily mean temperatures and not sensor uncertainty.The majority of comparisons of daily mean air temperature between all locations have an S1 File). While all room comparisons generated significant correlations for relative humidity values, stronger correlations existed between rooms on the same floor (R > 0.8). These strong correlations between sample locations indicate that the largest influence on room relative humidity values was the common HVAC system, with little effect from environmental conditions, occupancy, or occupant activity. Overall, ∼66.8% of all measured relative humidity differences were found to be within the range of propagated instrument uncertainty , with 48.8% to 84.5% of measurement differences falling within this range of uncertainty for individual room comparisons (Table D in S1 File). About 75% of the daily average measurements fell within this range of uncertainty within patient rooms and nurse stations on the same floor. The large number of comparisons being within the range of instrument uncertainty supports the strong correlations.In contrast with air temperatures, strong correlations in daily mean relative humidity values were observed in patient rooms and nurse stations on both floors, with correlation coefficients typically between 0.7 and 0.8 (S1 File). As with relative humidity, room humidity ratio comparisons were again all significant, and the highest correlations also occurred between rooms on individual floors. The nurse station and rooms on the same floors all had correlation coefficients of at least 0.99. Correlations were slightly weaker between floors. About 63.4% of all measured humidity ratio differences were found to be within the range of propagated instrument uncertainty , with 24.4% to 98.1% of measured differences falling within this range of uncertainty for individual location comparisons (Table D in S1 File). Within patient rooms and nurse stations on the same floor, about 85% of the daily average measurements fell within the range of uncertainty. The large number of comparisons with differences within the range of instrument uncertainty again supports the strong correlations between humidity ratio measurements. These data further suggest that absolute humidity in the hospital was tightly controlled by the HVAC systems and that the HVAC systems on both floors operated in a similar fashion.Daily average humidity ratios showed even stronger correlations between rooms and nurse stations (S1 File). No correlation was apparent between nurse stations and rooms on the same floor, with nurse station illuminance levels remaining relatively low compared to rooms, as expected due to the absence of immediate external windows in nursing stations. Correlation coefficients across all patient rooms and nurse stations reveal a wide variation both within and between floors. Correlation coefficients were as low as 0.18 between patient rooms and as high as 0.55 between floors; although nearly all correlations between patient rooms were statistically significant. Nurse stations on both floors were not significantly correlated with either other patient rooms or the other nurse station. Uncertainty in illuminance measurements was not explored because the manufacturer did not report sensor accuracy.Daily average illuminance levels between rooms on the same floor appeared to show moderate correlations, particularly at lower illuminance levels (S1 File). However, correlations were slightly stronger for rooms on the same floor as opposed to rooms between different floors. Between floors, correlation coefficients ranged from as low as 0.02 to as high as 0.34. Only 9 out of 45 room comparisons (20%) revealed significant correlations. Uncertainty in IR beam-break measurements was not explored because the nature of the measurement did not allow for direct comparisons. Overall, very weak correlations were observed between daily total IR beam-breaks in individual rooms, suggesting that the patient rooms were independently highly variable in terms of human occupancy and activity.Correlations between daily total IR beam-break counts among individual rooms were extremely weak and mostly insignificant . Only 8 out of 45 room comparisons (18%) showed statistically significant, albeit weak, correlations. Only three of these significant correlations came from the same patient rooms that had significant correlations in daily total IR beam-breaks, suggesting that the two methods cannot be used interchangeably to assess occupancy. An average of 94.3% of all measured differences in daily average room-source CO2 concentrations between rooms was found to be within the range of propagated uncertainty , with 86.7% to 100% of measured differences falling within this range of uncertainty for individual room comparisons (Table D in S1 File). That the vast majority of values were within instrument uncertainty limits confidence in our ability to distinguish differences in room-source CO2 concentrations (and associated occupancy estimates) between patient rooms.Room-source COFig. 6). Conversely, humidity ratios had a larger range on the lower floor and slightly lower median values compared to the upper floor. The difference is intuitive given the effect from the HVAC systems and the fact that the two floors are served by different air handling units that operated at different outdoor air fractions during some outdoor climate conditions. The lower floor had both a lower median outdoor air fraction and a wider range of outdoor air fraction values , which is consistent with information obtained from the building’s facilities manager. Daily illuminance levels between floors was very similar, which is reasonable given that all rooms were west-facing and have similar elevations and fenestration ratios. Distributions of hourly IR beam-break counts and CO2 concentrations were very similar between floors, indicating that aggregate occupancy and activity between floors were very similar .The range of daily average air temperatures was slightly higher on the upper floor compared to the lower floor, with both higher median  and maximum temperatures . However, as expected, illuminance levels were much higher in all locations during daytime periods  across all locations. Also quite intuitively, both hourly total IR beam-break counts and hourly average CO2 concentrations in the patient rooms were moderately higher during daytime periods .Hourly average air temperature, humidity ratio, relative humidity (not shown but very similar to humidity ratio), and outdoor air fractions revealed no differences between nighttime and daytime periods  is a multidisciplinary study of the hospital built environment. While efforts are still underway to describe the ecology of microbial communities that colonized and spread throughout the hospital, the analysis of the built environment data herein can provide insight into the associated dynamics of this new building. The built environment data collection campaign generated approximately 8×106 data points covering a range of building environmental and operational parameters that may have influenced occupant comfort and health, indoor environmental quality, and the growth, survival, and succession of microbes within the building. Within-room, between-room, between-floor, and temporal variations in individual parameters revealed interesting, sometimes intuitive, patterns. For example, patient room and nurse station air temperatures varied more than expected for such a typically tightly controlled environment . Effects from occupant temperature control clearly contributed to the range and variation within patient rooms and thus the low correlations between rooms and nurse stations. Relative humidity and humidity ratios were both strongly correlated between patient rooms, indicating a strong effect from the HVAC system and very little effect from occupants and activities, particularly for humidity ratio, which is not dependent on temperature. The time-series data indicated that the indoor humidity ratio was tightly controlled during summer and, to a lesser extent, during winter months when the weather was most extreme in Chicago. Between those seasons, humidity ratios varied more widely, indicating a lack of central control during transition months and swing seasons. However, the HVAC system appeared to be designed and operated to limit indoor humidity ratios to less than ∼9 gw/kgda, regardless of how high outdoor humidity ratios reached . Illuminance levels showed moderately strong correlations between the perimeter patient rooms and weak correlations to the interior nurse stations, which is likely attributed to similar solar exposure on the west-facing facades among all patient rooms. Differences between patient rooms likely also stemmed from occupant control over both window shades and artificial lighting.The Hospital Microbiome Project . For example, the distributions and median values of hourly average relative humidity appear similar across all locations, whereas the weekly average relative humidity values exhibit more variation between locations.2 concentrations and IR beam-break data appeared to serve as useful surrogates for occupancy and activity. Daily averages of patient room-source CO2 concentrations and daily totals of IR beam-break counts were reasonably well correlated (more so in some rooms than others), suggesting that we were able to provide some helpful metric of both occupancy and activity. With a median of approximately 200 doorway beam-breaks per day , the patient rooms appear to be relatively lightly occupied compared to other types of environments, such as retail buildings, but tend to have fewer vacant periods than many offices of similar sizes [The combination of “room-source” COar sizes ,26.Overall, many of the indoor environmental conditions were fairly consistent with other long-term or large-sample studies. For example, measured air temperatures, relative humidity, and humidity ratios were generally in the range of those measured in a large sample of nearly 100 U.S. office buildings , but tem3/hr, providing ∼7–8 air changes per hour (ACH) of HEPA-filtered supply air in each room . Designes 6 ACH ).Environmental conditions such as temperature, relative humidity, and humidity ratio have been shown to influence both surface-bound and airborne microbes in varying ways, including varying growth or survival responses by viruses, bacteria, and fungi (including infectious agents) under different conditions ,31–46. Ow/kgda and 8 gw/kgda. This relatively large range of relative humidity and humidity ratio values is likely to have influenced both viral and bacterial survival in the hospital, given findings from a number of controlled studies in the literature [The range of air temperatures measured in this project  was typically only 7°C (from 19°C to 26°C) across all patient rooms, and even smaller at the nurse stations. It is not immediately clear how these small-magnitude differences may have influenced microbial growth/survival or infectious disease transmission, although this is the focus of an ongoing investigation. Hourly average relative humidity values ranged as much as 50% absolutely, with the majority of measurements between 25% and 50% across all patient rooms and nurse stations. Variation in humidity ratios was similar, with the majority of measurements occurring between 5 gterature ,36–46. Hterature   and increasing infection risk from one occupant to the next [Perhaps the largest potential contributor to between-room variation in indoor environmental quality and microbial community structure are the human occupants –51. Occuthe next –56. Thesthe next .2 sensors were useful in supplementing the people counters to measure occupancy, but had much higher costs and more downtime for maintenance due to additional components such as an absorber column and sampling lines. Additionally, their relatively high power requirements meant we were limited to sampling where electrical power was available. Finally, differential pressure sensors used were relatively low cost , but also required an external data logging device, large battery packs, and external sampling lines.We have demonstrated a systematic way to gather potentially valuable building environmental data with minimal costs and invasiveness, but at very high temporal and spatial resolution within the hospital. The use of off-the-shelf sensors to measure temperature, relative humidity, and illuminance levels with built-in data logging capabilities reduced the entailed cost and maintenance throughout the project. The IR beam-break people counters entailed lower costs and provided insight on human activity, but also gave a less accurate measure of occupancy, due to the non-directional rather than bi-directional functionality. COIt should be noted that the findings from this investigation are subject to a certain degree of error and uncertainty from factors such as sensor accuracy and uncertainty, measurement error , and others. Furthermore, the large number of data points taken and comparisons made can affect the results of statistical analyses . However, the results from this investigation can be accepted with a reasonably high degree of accuracy and provide extensive information on building environmental and operational parameters and a robust characterization of a typical hospital environment.Overall, using a suite of off-the-shelf sensors, we were able to measure multiple parameters continuously at 5-minute intervals over the span of approximately one year, providing millions of built environment data points for future comparisons. While this suite of measurements and methods provided a large robust dataset, additional development of lower cost sensors using common data collection platforms and remote uploading to the Internet should be prioritized in future investigations. Other possible improvements include using directional IR beam-break counters or other methods for assessing human occupancy, the installation of pressure taps to measure any changes in supply and recirculated airflow rates over time, measurements of equilibrium relative humidity on sampled surfaces (a surrogate for water activity), and more detailed measurements of human proximity near sample surfaces. The findings and recommendations in this study can be used to inform future investigations, and thus improve our understanding of the built environment.S1 Fig2 measurements were made (1) in the center of the room, (2) near the west-facing window, (3) under a cabinet , and (4) on the countertop near the doorway.CO(TIF)Click here for additional data file.S2 FigThe HVAC systems operated in economizer mode, varying OA fraction with outdoor temperature conditions.(TIF)Click here for additional data file.S3 FigThe HVAC systems operated in economizer mode, varying OA fraction with outdoor temperature conditions.(TIF)Click here for additional data file.S4 Figth floor than the 10th floor.OA fractions in the HVAC systems were typically lower on the 9(TIF)Click here for additional data file.S5 Fig2 concentration, pressure difference between room and hallway, and HVAC outdoor air (OA) fraction for one room on one floor.Example time series of 5-minute data for temperature, relative humidity, humidity ratio, illuminance, doorway IR beam-breaks, room air CO(TIF)Click here for additional data file.S6 Figw/kgda.There was no correlation between indoor temperature and outdoor temperature. Indoor humidity ratio tracked outdoor humidity ratio until the outdoor humidity ratio reached ∼8 g(TIF)Click here for additional data file.S7 FigDaily average air temperatures were weakly correlated between locations. The inset table shows pair-wise Pearson correlation coefficients for each location comparison.(TIF)Click here for additional data file.S8 FigDaily average relative humidity measurements showed strong correlations between locations. The inset table shows pair-wise Pearson correlation coefficients for each location comparison.(TIF)Click here for additional data file.S9 FigDaily average absolute humidity ratio measurements showed very strong correlations between locations. The inset table shows pair-wise Pearson correlation coefficients for each location comparison.(TIF)Click here for additional data file.S10 FigDaily average illuminance levels were moderately correlated between patient rooms, with no correlation between nurse stations. The inset table shows pair-wise Pearson correlation coefficients for each location comparison.(TIF)Click here for additional data file.S11 FigDaily total doorway IR beam-breaks showed little correlation between patient rooms. The inset table shows pair-wise Pearson correlation coefficients for each location comparison.(TIF)Click here for additional data file.S12 Fig2 concentrations showed little correlation between patient rooms. The inset table shows pair-wise Pearson correlation coefficients for each location comparison.Daily average room-source CO(TIF)Click here for additional data file.S1 File2 (and occupancy) in the patient rooms. Table D, Fractions of measured differences in daily mean temperature, relative humidity, and humidity ratio between patient rooms and nurse stations, as well as room-air CO2 concentrations in the patient rooms, that were within the range of propagated uncertainty.This file also contains Tables A-D. Table A, Flow schedule and baseline measurements. Table B, Pair-wise correlation matrices for daily mean air temperature, relative humidity, humidity ratio, and illuminance levels in the patient rooms and nurse stations. Table C, Pair-wise correlation matrices for daily total IR beam-breaks and daily average room-source CO(DOC)Click here for additional data file.


Alternanthera mosaic virus (AltMV) infecting Achyranthes bidentata was first detected in Asia, and the complete genome sequence  was determined. Sequence identity analysis and phylogenetic analysis confirmed that this isolate is the most phylogenetically distant AltMV isolate worldwide. Alternanthera mosaic virus (AltMV) is a member of the genus Potexvirus in the family Alphaflexiviridae and has flexuous filamentous particles and a single-stranded positive-sense RNA genome. The AltMV genome is about 6.6 kb in size and contains five open reading frames (ORFs). AltMV was first described from Alternanthera pungens (family Amaranthaceae) in Australia  is a perennial herb distributed widely in Asia. In 2015, A. bidentata leaves showing mosaic symptoms were collected in Tokyo, Japan. The presence of potexvirus-like particles with lengths of 500 to 600 nm was confirmed by electron microscopy. Virion purification and viral RNA extraction were performed, as described previously , as previously described (The sequence identities between AltMV-Ac and other AltMV isolates were calculated using the program SDT  based onLC107515.The genome sequence of AltMV-Ac has been deposited into DDBJ under accession number


Phenotype-environment correlations and the evolution of trait interactions in adaptive radiations have been widely studied to gain insight into the dynamics underpinning rapid species diversification. In this study we explore the phenotype-environment correlation and evolution of operculum shape in cichlid fishes using an outline-based geometric morphometric approach combined with stable isotope indicators of macrohabitat and trophic niche. We then apply our method to a sample of extinct saurichthyid fishes, a highly diverse and near globally distributed group of actinopterygians occurring throughout the Triassic, to assess the utility of extant data to inform our understanding of ecomorphological evolution in extinct species flocks. A series of comparative methods were used to analyze shape data for 54 extant species of cichlids (N = 416), and 6 extinct species of saurichthyids (N = 44). Results provide evidence for a relationship between operculum shape and feeding ecology, a concentration in shape evolution towards present along with evidence for convergence in form, and significant correlation between the major axes of shape change and measures of gut length and body elongation. The operculum is one of few features that can be compared in extant and extinct groups, enabling reconstruction of phenotype-environment interactions and modes of evolutionary diversification in deep time. Understanding how organismal diversity is generated and maintained, why some groups diversify when others remain relatively unchanged over geological time, and how organisms adapt to and interact with the environment are key challenges in evolutionary biology. Adaptive radiations, defined as rapid and extensive diversifications from an ancestral species that result in descendants adapted to exploit a wide array of ecological niches1The species flocks of cichlid fishes from the East African Great Lakes collectively represent an unparalleled example of adaptive radiation in vertebrates34589108121415171818192021Among the morphological traits examined in LT cichlids so far, most comprise combinations of linear measurement or scored character data and, with the notable exception of body shape and size measures, few traits are directly amenable to comparison with other species flocks, such as sticklebacks and Antarctic notothenioids, which represent radiations of different geological age that have occurred in different environmental settings . Uncovering commonalities in trait complex evolution in phylogenetically, morphologically and ecologically distinct species flocks would be highly desirable in assessing key questions underpinning how adaptive radiation progresses. Issues include the general extent to which diversification occurs in stages2325In this paper, we build upon our earlier geometric morphometric investigations of operculum shape in extant303433363839Saurichthys  and the Prosanto formation , thereby allowing for detailed palaeoecological, faunal and stratigraphical information to be extracted for numerous species . Noteworthy is that the PC1-PC2 plot  largely reflected shape changes occurring along the dorsal edge of the bone, resulting in a more asymmetrical shape of the bone at the dorsal margin, either angled with more bone extending on the anterior (negative PC3 score) or posterior (positive PC3 score) side  side . PC3 resAltolamprologus compressiceps [altcom], Lamprologus ornatipinnis [lamorn] and A. fasciatus [altfas]), respectively, to species with larger bones showing expansion of the dorsal anterior margin and compression of the dorsal posterior margin   .Perissodus) and zooplankton feeders, both of which are located at the positive end of CV2, from piscivores and generalist feeders, which both have negative scores for CV2  between microvertebrate/algae eaters (negative scores) and piscivores (positive scores), with generalist and benthic invertebrate feeders occupying an intermediate position between the two. CV1 largely reflected shape change along the posterior margin of the operculum, with piscivores having a dorsally-broader bone that tapers postero-ventrally. CV2 (18.0%) separates the scale eaters  clearly separated the ram and suction feeders, which are shown to have a dorsally flattened opercle margin and general wider bone, from species that sandpick, algaepick and rockpick  and feeding preference on operculum size and shape .CVA conducted on feeding mode revealed a clear separation between all feeding mode categories with the exception of benthic invertebrate pickers and suction groups that overlapped almost completely in CV1-CV2 morphospace . CV1 , and centroid size  and PC2  were correlated with ER, however these correlations were not significant. The sauricthyids were more elongate  than the LT cichlids studied here, and PC1  or values of δshown in  for the shown in . Among tshown in  and is lModel fitting results indicated that PC axes of operculum shape showed best fit to different models. In contrast to the other shape variables, PC1 was best fit by the WN model, however AICc values showed very small magnitudes of difference between that model and all others (∆AICc = 0.26–0.49), apart from BM, which was least favored (∆AICc = 5.20) . Pagel’sK values were less than 1 for all examined axes of shape space, and for centroid size  to 0.59 (centroid size). Generally, values of K were quite low for PC axes, and lowest for PC1, reflected also in the low Akaike weight (probability 0.02) of that model. Our reported range of 0.34 (PC1) to 0.42 (PC2) for K corresponds well with that of earlier reported values for PC axes of body shape (range = 0.41–0.44), and is lower than that for PC axes of lower pharyngeal jaw (LPJ) shape (range = 0.48–0.67)  19. Pagel48–0.67) . Values Values for α from EB model fitting were positive for all shape axes and centroid size, indicating acceleration in trait evolution. Pagel’s δ values were also greater than 1 for all measured axes and centroid size (range 5.06–8.67); δ values >1 indicate a concentration of evolution in the operculum shape and size traits towards present. If Pagel’s δ values were <1 this would indicate that branch lengths are transformed to become increasingly shorter towards the tips, meaning that trait change occurred mainly along basal branches. Operculum shape appears to be evolving more rapidly than size, as indicated by larger values for α. The time-dependent models were not better fit than alternative tested models in the case of centroid size. In contrast, for PC1 and PC2, Pagel’s δ, EB and OU appear to be considerably better supported (∆AICc >4) then the BM model .Pairwise distance-contrast plots indicate a general trend that is compatible with convergence, showing most species pairs occupy the quadrant of the plot represented by small morphological distances yet large phylogenetic distance . SimilarP = 0.0049) and size (P < 0.001). The morphological disparity index (MDI), which indicates the amount of difference in disparity between observed trait data and data expected under BM, was greater for operculum size (MDI = 0.35) than for shape data (MDI = 0.15). Neither plots show clear evidence for an Early Burst in these traits, which is in accordance with the above reported α values for the EB calculations and the Pagel’s δ values, which together point towards more evolutionary change in the recent fauna for operculum size and shape.Disparity through time analyses resulted in generally similar patterns of average clade disparities for shape and size across the time slices plotted . Shape dThe species flock of LT cichlids is well-recognized as an ideal model system for studying how organismal diversity emerges54915N point towards support for a relationship between operculum shape and feeding, which is shown by the results of the Procrustes ANOVAs and CVAs using feeding mode and preference , and the Urweltmuseum Hauff . These six species encapsulate the full range of body size variation within the clade, including both small-bodied Saurichthys striolatus . Sampling was chosen to maximize usage of available data for body elongationThe study sample comprised 416 specimens (54 species), representing 31 genera (of 53) and 11 of the 14 tribes present in the lakeEach specimen was photographed according to a standard procedure that has been used previously for geometric morphometric studies of the operculum2828ednrb1, phpt) markers  to extract axes of maximum shape variance in the sample, and the broken stick model markers .Landmark data for all saurichthyid specimens (N = 44) were inputted into a separate PCA to extract the main axes of shape variance, and mean PC scores for six saurichthyid species were used in subsequent data plots.Canonical variates analysis (CVA) of species’ mean landmark data was used to visualize the extent to which operculum shape reflected feeding preference and feeding mode groupings in LT cichlids. Data for feeding preference were collated from the literature6970717273747513C, δ15N, gill raker number on the ventral arch, gill raker number on the dorsal arch, average gill raker length, and gut lengthet al. in prep). All seven traits were available for LT cichlids, and ER was available for the fossil saurichthyid sample. Stable isotopes for δ13C and δ15N were used as proxies for specialization along the benthic-limnetic axis (macrohabitat) and tropic niche (microhabitat), respectivelyet al. in prep). Elongation ratio (ER) data were taken from Maxwell and WilsonSeven traits were used as covariates in this study: δnlmeet al.Using species mean values, interactions between operculum shape (PC1 and PC2) and form (PC1) space axes and centroid size in relation to the above seven traits were examined using Phylogenetic generalized least squares (PGLS) regression. PGLS takes phylogenetic relationships into account, assuming that the evolution of residual traits follows a neutral model (Brownian motion)79GeigerSeveral models were fit to the LT cichlid operculum shape data (axes PC1-PC3) and centroid size data, using the fitContinuous function in the R package The time-dependence of trait evolution was assessed using Pagel’s δ modelK statisticPicanteK statistic indicate that close relatives are more similar in operculum traits than expected given the topology and branch lengths, whereas values of <1 indicate less phylogenetic signal than expected under a Brownian motion modelTo quantify phylogenetic signal in operculum shape (axes PC1-PC3) and centroid size data, Blomberg’s Tylochromis polylepis (tylpol) was removed from the data set due to its large distance from other taxaGeigerPairwise distance-contrast plots were constructed following a similar approach to Muschick and colleaguesGeigeret al.P value. To correct for tip over dispersion, MDI values were calculated over the first 90% of the phylogeny.To evaluate how operculum size and shape disparity changed through time, disparity through time (DTT) analyses were implemented in the R package How to cite this article: Wilson, L. A. B. et al. Evolution of opercle shape in cichlid fishes from Lake Tanganyika - adaptive trait interactions in extant and extinct species flocks. Sci. Rep.5, 16909; doi: 10.1038/srep16909 (2015).


S. cerevisiae, both response and resistance to stressors that activate signaling pathways differ between diverse isolates. Here, we present a quantitative trait locus (QTL) mapping approach that enables us to identify genetic variants underlying such phenotypic differences across the genetic and phenotypic diversity of S. cerevisiae. Using a Round-robin cross between twelve diverse strains, we identified QTL that influence phenotypes critically dependent on MAPK signaling cascades. Genetic variants under these QTL fall within MAPK signaling networks themselves as well as other interconnected signaling pathways. Finally, we demonstrate how the mapping results from multiple strain background can be leveraged to narrow the search space of causal genetic variants.Signaling pathways enable cells to sense and respond to their environment. Many cellular signaling strategies are conserved from fungi to humans, yet their activity and phenotypic consequences can vary extensively among individuals within a species. A systematic assessment of the impact of naturally occurring genetic variation on signaling pathways remains to be conducted. In  Wild yeast strains differ in phenotypes that are controlled by highly conserved signaling pathways. Yet it remains unknown how naturally occurring genetic variants influence signaling pathways in yeast. We have developed an approach to facilitate the mapping of genetic variants that underlie these phenotypic differences in a set of wild strain. Our mapping approach requires minimal strain engineering and enables the rapid isolation of mapping populations from any strain background. In particular, we have mapped genetic variants in twelve highly diverse yeast strains. Further, we demonstrate how the mapping results from these twelve strains can be used jointly to narrow the number of genetic variants identified to a set of putative causal variants. We identify genetic variants in genes with various roles in cell signaling. Our results illustrate the interplay of different signaling pathways and which signaling genes are more likely to contain variants of large phenotypic impact. Saccharomyces cerevisiae has long been used as a eukaryotic model system to elucidate the principles of these signaling pathways, as many of the core signaling proteins are conserved from yeast to human Cellular survival is dependent on the ability to sense and respond to changing environmental conditions. Mitogen-activated protein kinase (MAPK) signaling cascades are ubiquitous in eukaryotic organisms and enable them to react to extracellular stimuli In yeast, MAPK pathways facilitate response to environmental cues such as the presence of mating pheromones and stresses such as cell wall damage and high osmolarity S. cerevisiae have characterized the genetic architecture of complex traits, including global gene expression and resistance to small molecules S. cerevisiae species-wide genetic and phenotypic variation and have left much of the genetic architecture of quantitative traits across the species unexplored.Yeast is an ideal model system for the study of quantitative traits. Quantitative trait locus (QTL) mapping studies in S. cerevisiae shapes MAPK-dependent traits. Finally, we illustrate how a round-robin cross design can be leveraged to identify causal genetic variants.Here, we introduce a novel X-QTL approach and use it to map variants that alter MAPK-dependent traits in a panel of diverse yeast isolates. Our method takes advantage of plasmid-borne fluorescent markers to leverage the precision and high throughput of fluorescence-activated cell sorting (FACS) to rapidly generate large mapping populations of recombinant haploids. We charted the genetic architectures of salt (sodium chloride) and caffeine tolerance in a set of strains that capture much of the species-wide genetic diversity. Salt tolerance is mediated by the HOG MAPK pathway, while exposure of yeast to caffeine results in the activation of the CWI MAPK pathway HIS3 under the control of the MATa-specific STE2 promoter for the isolation of MATa recombinant haploids S. cerevisiae are generally prototrophs, lacking the auxotrophic mutations required for the original X-QTL approach. In addition, auxotrophic mutations can have large phenotypic effects HIS3 deletion required under the original X-QTL approach results in sensitivity to various salts, which is not desirable when investigating natural variation in salt resistance STE2 promoter restricted X-QTL mapping populations to segregants of the MATa mating type. Here, we designed a novel approach that relies on mating-type specific expression of plasmid-borne fluorescent reporters, enabling the isolation of haploids of either mating type without the need to integrate the reporters into the genome . The resulting populations were bulk-sequenced to assess allele frequencies at SNP sites segregating in a particular cross. We used the MULTIPOOL software to calculate LOD scores testing whether allele frequencies at a given position in the genome in each selected population differed from those in the corresponding control population . [21], [We used replicate BY/RM mapping experiments to determine the robustness and reproducibility of our mapping approach see .. At a Lp<0.001). This suggests that many of these QTL stem from small effect variants that narrowly escaped detection in one of the two experiments.We applied our mapping strategy to identify QTL that influence MAPK-dependent resistance traits in the twelve Round-robin crosses. We used the MATa and MATα selections for each cross as replicate mapping experiments see . Indeed,Next, we asked how QTL are distributed and shared among the Round-robin crosses . The salp = 2.6×10−6). In addition, interactions between the QTL and the genetic background likely contribute to context dependency Because each genetic variant is introduced into the Round-robin scheme at least twice, each QTL is expected to be identified at least twice assuming 100% power and no genetic interactions. Contrary to this expectation, previous studies involving sets of interconnected crosses have found that the majority of QTL are ‘context-dependent’, that is their identification was dependent on a specific cross and not just on the parent strains involved We asked if the QTL mapping had resulted in any functional enrichment in the underlying genes. While genes within caffeine resistance QTL were only marginally enriched for the GO-term ‘Response to Stimulus’ (p = 0.077), genes found under salt resistance QTL were significantly enriched for ‘Cellular Response to Stimulus’ (p = 0.006) and ‘Kinase Activity’ (p = 0.002) . This suENA locus on chromosome 4, which we detected in eleven out of twelve crosses. The ENA genes encode sodium efflux pumps that, in the presence of high salt, are transcriptionally activated by the HOG pathway S. cerevisiae isolates carry an ENA variant that resulted from an introgression from the yeast species S. paradoxus and exists in varying copy numbers, referred to as ENA1 through ENA5. In contrast, non-European strains carry a single copy of the ancestral ENA6 gene ENA variants within our parent strains and found that strains with high salt resistance indeed carried multiple copies of the introgressed ENA gene , as well as between non-European isolates carrying alleles of the ENA6 gene that differed from each other by three missense mutations (Cross 12). Our findings corroborate the importance of the ENA locus in shaping the genetic architecture of salt resistance Several of the identified QTL tower among the genetic architectures of the two resistance traits . ToleranENA gene . InteresS. cerevisiae caffeine resistance architecture are the QTL containing TOR1 and TOR2 on chromosomes 10 and 11, respectively  and CWI sensors (WSC3 & MTL1) responsible for detecting the respective perturbations. Moving along the signaling cascades, we found several key regulators such as MSS4 and GPA1. Furthermore, one of the caffeine tolerance QTL contained SLT2, which encodes the MAPK at the center of the CWI response. Arriving at the output of the signaling pathways, we identified several genes transcriptionally activated in response to stress. In addition to the ENA genes, we found GPG1, which is activated by the HOG pathway to increase glycerol synthesis to counteract changes in osmolarity We have genetically dissected stress resistances mediated by two MAPK pathways, as well as interrogated the genetic contribution to the baseline cost of the mating pathway. We studied twelve diverse yeast strains that represent a large proportion of the genetic and phenotypic diversity of e levels . SeveralCYR1 and TOR2, core members of the cAMP-PKA and TOR pathways respectively, were tied to both stress responses. These pathways sense the internal availability of nutrients and repress the general stress response TOR gene. Our study links numerous alleles of TOR1 and TOR2 to differential growth under stress conditions. The two Tor proteins are redundant in regards to their participation in TORC1 complexes, but only Tor2p can partake in TORC2 complexes TOR genes are diverging at the same rate as the majority of duplicated gene pairs TOR alleles indicative of functional tradeoffs? Similarly, it bears determination whether stress sensitive alleles of the TOR2 QTL, which were found in natural rather than ‘domesticated’ isolates, are beneficial under conditions approximating their source environment.In addition to genes directly associated with MAPK pathways, we found genes that highlight the interconnectivity of the cellular signaling network. Variants in WHI2 further echoes the interplay between internal nutrient and external stress sensing pathways. While WHI2 is required for the activation of stress response pathways WHI2 loss-of-function alleles were found to be driver mutations in experimental evolution experiments whi2 mutants not only illustrate how laboratory evolution experiments can recapitulate the biology of wild isolates, but also suggest that the concomitant loss of stress responsiveness is adaptive under permissive growth conditions WHI2 allele responsible for stress sensitivity was advantageous for growth under the permissive YPD condition.The identification of SSK1, SSK2, STE11, PBS2, and HOG1; CWI: RHO1, PKC1, BCK1, MKK1, MKK2, and SLT2) are highly conserved across species. Analysis of the strain sequences revealed that despite their species-level conservation, all of these core proteins, with the exception of Rho1p, contain instances of non-synonymous coding variants. Yet, we only identified QTL at SLT2, indicating that the coding variants in the other core genes may be of little functional consequence.The QTL results provide insights into the types of genes that carry variants shaping MAPK-associated phenotypes of different yeast isolates. The core elements of the HOG and CWI signaling cascades . The BY/RM diploid was published previously . Primers used in allele replacement experiments are listed in We used the NatMX cassette from pUG74 et al.We phenotyped a set of strains previously described by Schacherer We modified a published random spore prep protocol In order to isolate individual spores or to assess the amount of spores isolated, dilutions of the spore prep (for most crosses 25 µl spore mix in 250 µl H2O was used) on the plates containing 25 mg/L ClonNAT or 200 mg/ml G418. Individual haploids were picked after two days of growth at 30°C using a fluorescence-equipped dissection microscope. For cell sorting 250 µl of the samples was plated on four plates containing the appropriate drug to select for the marker plasmid.Spore preps were harvested after 2 days of growth at 30°C. Cells were washed off plates using 5 ml water and collected in 50 ml conical tubes. Cells were sonicated for 1 min at power level 2 and then diluted to an OD of 0.4 for FACS in 1xPBS. We used a BD FACSVantage SE to sort 500,000 green and red fluorescent cells each into vials containing 2 ml YPD with 100 µg/ml ampicilin to prevent bacterial contamination during the cell sorting. The sorted cells were added to 10 ml YPD containing 100 µg/ml ampicilin in glass tubes and cultured for 6 hours at 30°C. Cultures were spun down for 5 minutes at 3,000 rpm and after removal of supernatant cells were resuspended in 950 µl water. 200 µl of cells suspension were then plated on 5 different conditions: YPD, YPD with 0.5 M NaCl, YPD with 1 M NaCl, YPD with 15 mM caffeine, and YPD with 20 mM caffeine. Plates were grown for 2 days at 30°C. Plates were made by adding the appropriate amount of NaCl or caffeine to the standard YPD recipe prior to autoclaving.et al.We assessed to what extent segregant populations grew under the selective conditions and proceeded with conditions that resulted in a reduced yet adequate amount of growth compared to the permissive YPD condition Sequencing libraries were generated using Epicentre Nextera DNA Sample Prep kits or Illumina Nextera Sample Prepartation kits as described previously We created a set of genetic markers for each cross by aligning parent strain sequences to the sacCer3 reference genome and we then used custom scripts written in the programming language R to identify SNPs that differed between any set of parent strains. We then determined allele frequencies at these variant sites. We subtracted the YPD allele frequencies at each variant position from those obtained under the different growth conditions to determine the allele frequency skews that are due to a specific condition rather than permissive growth. To determine allele frequency differences due to mating type, allele frequencies from MATα samples were subtracted from those of corresponding MATa samples.We converted allele frequency skews into LOD scores using the MULTIPOOL method We used a set of replicate BY/RM mapping experiments to determine the false discovery rate and reproducibility of our mapping approach. We generated allele frequency measurements for four independent experiments using two independent transformants of the BY/RM diploid with the mating type marker plasmid. We isolated mapping populations of either mating type and used the same selection conditions as for the round-robin crosses . The YPD 20 mM Caffeine condition resulted in very low growth of BY/RM segregants and we did not proceed further with this condition. Given the two mating type mapping populations and four independent experiments, we generated eight selections for each condition. We compared the allele frequencies resulting from technical replicate experiments stemming from one transformant (16 comparisons) and found no allele frequency fluctuations that resulted in LOD scores higher than 2.62. Analysis of biological replicates, i.e. comparing experiments based on different transformants, resulted in six peaks with LOD scores higher than our threshold of LOD 5. Yet, five of these peaks were actually the same region identified multiple times in different replicate pairs suggesting that they were due to a genetic difference between the two BY/RM transformants and not due to random allele fluctuations. The remaining peak tagged the mating type locus on chromosome 3 pointing towards a difference in the mating type selection rather than a random allele frequency fluctuation.In order to asses the reproducibility of the mapping approach we determined condition-specific QTL for individual experiments and then asked to what extent QTL where found in pairs of replicates. To keep this assessment of reproducibility consistent with the round-robin mapping experiment, we did not ask whether QTL were identified multiple times across all replicates. Such a measure of reproducibility would be inflated in comparison to experiments with only two replicates, such as the experiments with the round-robin crosses. Rather we tested if QTL were reproducibly identified in designated pairs of experiments, either technical replicates performed with the same strain and the same condition or the experiments of opposite mating types of one replicate. At a LOD threshold of 5, 90.1% of QTL were reproducibly identified between technical replicates of the same mapping experiment. Reproducibility was 88.3% using mapping populations of opposite mating type. For the experiments with technical replicates, the directions of the allele effects of the QTL identified in only one experiment were consistent among replicates in 100% of the cases. This suggests that these QTL only narrowly missed the LOD threshold in replicate experiments. Indeed, their LOD scores were close to the threshold of LOD 5 (median LOD 5.9 vs. 9.84 for replicated QTL). For the round-robin experiments across all conditions, 74.4% of QTL  were identified in both of the mapping experiments. Since QTL that narrowly miss the LOD threshold are classified as not being reproducible, we asked to what extent allele frequency directions, in the control case BY versus RM, agreed over the identified region. We determined the mean allele frequencies for the region underlying a QTL and compared them across the pairs of replicate experiments. For the BY/RM experiments, the sign of the allele frequencies agreed for all eleven QTL that had missed replication. In the case of the round-robin crosses, allele frequency directions agreed for 93.5% of non-replicated QTL. This result was robust to permutation of the allele frequencies (p<0.001).We used VEP We used Cortex S1 FigA) 1 M NaCl and (B) 20 mM Caffeine. Parent strains were phenotyped using five biological replicates. Segregants and parents of crosses 4, 7 and 9 were tested separately from those of the other crosses; hence the phenotypes of shared parent strains are not perfectly matched. Growth on the restrictive conditions was normalized using growth under the permissive YPD condition.Phenotyping of individual Round Robin segregants. Between 17 and 20 recombinant haploids were isolated for each of the twelve round robin crosses and, in conjunction with the haploid parent strains, phenotyped as to their growth in the presence of ((TIF)Click here for additional data file.S2 FigA) Allele frequency plots for a BY/RM MATa caffeine selection and its corresponding YPD control experiment. Loess-smoothed BY allele frequencies across the genome are plotted. (B) To account for growth effects under the permissive YPD growth condition, allele frequencies distributions resulting from growth on YPD are subtracted from those produced by the selective condition. Grey points represent individual alleles at which allele frequencies were measured and illustrate the dense coverage of these genetic markers. (C) Allele frequencies are converted into LOD scores using the MULTIPOOL software Conversion of allele frequencies to LOD scores. ((TIF)Click here for additional data file.S3 FigA) salt and (B) caffeine. The experiments were carried out in duplicate for each of two independent transformants of the BYxRM diploid with the mating type marker construct. MATa and MATα selections experiments for each provide for further replication, resulting in a set of eight replicates for each condition. Lines represent moving averages of the allele frequency spectra and are overlaid to illustrate their reproducibility. LOD score plots of replicate (C) sodium chloride and (D) caffeine selection experiments illustrate the high reproducibility of our QTL mapping approach. Each selection was carried out in quadruplicate for each mating type. The dashed horizontal line indicates the LOD threshold of 5. Tick marks on the upper axis indicate peak positions of QTL identified.Allele frequency and QTL plots of replicate BY/RM selection experiments. Genome-wide allele frequency distributions were determined in eight replicate selection experiments each for ((TIF)Click here for additional data file.S4 FigA) salt and (B) caffeine, are jointly plotted. The dashed horizontal line indicates the LOD threshold of 5. The 2-LOD confidence intervals of the QTL identified are indicated using vertical orange bars. For Cross 4 under the 1 M sodium chloride selection was very restrictive resulting in a low number of individual segregants in the genotyping pool and hence less reproducible LOD scores. Yet, the QTL confirmed by the replicate selection experiments were also detected using a less restrictive selection condition of 0.5 M sodium chloride and largely (7 of 9 QTL) shared with other crosses.Individual LOD plots for each of the round-robin selection experiments. LOD plots of the MATa (Green) and MATα (Red) selection experiments for each cross and condition, ((TIF)Click here for additional data file.S1 TableStrain phenotypes. Each measurement is the average growth of four technical replicates adjusted for growth on YPD alone. The average of the two biological replicates is plotted in (XLSX)Click here for additional data file.S2 TableSummary of RR segregant data and corresponding QTL patterns. This table lists the phenotype distributions of individual segregants from the round-robin crosses. We tested for interactions between the underlying causal loci using an epistasis test described by Lynch and Walsh (XLSX)Click here for additional data file.S3 TableReplicate confirmed BY/RM QTL. This table lists the QTL that passed the LOD 5 threshold and were identified in at least one pair of technical replicates.(XLSX)Click here for additional data file.S4 TableGrouped RR QTL. QTL: QTL identifier; LOD score: Combined LOD score of grouped QTL; Max LOD position: Position with highest LOD score across grouped QTL; Crosses with QTL: Round-robin crosses in which the QTL was detected; Lower/upper bound of QTL: 2-LOD confidence interval of QTL; Number of Coding Variants in QTL: Non-synonymous coding variants within the QTL interval across the RR parent strains; Significant Variants: Number of non-synonymous coding variants with a significant association score; Association Score Coding Variants: Maximum association score among non-synonymous coding variants; Association Score All Variants: Maximum association score among all variants within the QTL interval: Candidate QTG Candidate quantitative trait genes based on Saccharomyces Genome Database gene annotations.(XLSX)Click here for additional data file.S5 TableGO Analyses. SGD GO Analyses based on non-synonymous coding variants within the identified QTL intervals.(XLSX)Click here for additional data file.S6 TableENA2 (SGD S288c YDR039c) or ENA6 (GenBank: FJ228230.1). We calculated the median coverage of these two variants and compared it to the median coverage of chromosome IV, on which both are located, in order to determine their copy number.Copy number of ENA variants in the RR parent strains. For each strain sequences were aligned to the coding sequence of (XLSX)Click here for additional data file.S7 TableAlleles underlying Round-robin QTL. Table cataloging which strains contribute beneficial or detrimental alleles to each QTL.(XLSX)Click here for additional data file.S8 TableRound-robin MAT QTL. QTL identified in multiple conditions are indicated in bold. We excluded the 1M NaCl selections for Cross 11 from this analysis as this selection was stringent for this cross and few cells grew under this condition. While comparison of the MATa and MATx selections did identify shared QTL, QTL not shared between the two mating types are likely false positives resulting from the small number of segregants in the mapping pool.(XLSX)Click here for additional data file.S9 TablePlasmids. All yeast expression vectors carry the AmpR gene. Plasmids are available on Addgene.(XLSX)Click here for additional data file.S10 TablePrimers. Table listing the primers used in this study.(XLSX)Click here for additional data file.


Bone metastasis occurs for men with advanced prostate cancer which promotes osseous growth and destruction driven by alterations in osteoblast and osteoclast homeostasis. Patients can experience pain, spontaneous fractures and morbidity eroding overall quality of life. The complex and dynamic cellular interactions within the bone microenvironment limit current treatment options thus prostate to bone metastases remains incurable. This study uses voxel-based analysis of diffusion-weighted MRI and CT scans to simultaneously evaluate temporal changes in normal bone homeostasis along with prostate bone metatastsis to deliver an improved understanding of the spatiotemporal local microenvironment. Dynamic tumor-stromal interactions were assessed during treatment in mouse models along with a pilot prospective clinical trial with metastatic hormone sensitive and castration resistant prostate cancer patients with bone metastases. Longitudinal changes in tumor and bone imaging metrics during delivery of therapy were quantified. Studies revealed that voxel-based parametric response maps (PRM) of DW-MRI and CT scans could be used to quantify and spatially visualize dynamic changes during prostate tumor growth and in response to treatment thereby distinguishing patients with stable disease from those with progressive disease (p<0.05). These studies suggest that PRM imaging biomarkers are useful for detection of the impact of prostate tumor-stromal responses to therapies thus demonstrating the potential of multi-modal PRM image-based biomarkers as a novel means for assessing dynamic alterations associated with metastatic prostate cancer. These results establish an integrated and clinically translatable approach which can be readily implemented for improving the clinical management of patients with metastatic bone disease.NCT02064283ClinicalTrials.gov  It issphonate –10. Plaisphonate ,12 have sphonate . HoweverDiffusion-weighted MRI (DW-MRI) has been reported as a tool for assessing cancer response to therapy as it is able to quantify the random  motion of water molecules within tissue –18. WateMore recently, successful use of DW-MRI and the fDM metric for providing early indication of treatment response in preclinical models as well as patients diagnosed with metastatic prostate cancer to the bone have been reported ,35–37. FADC) along with changes in bone density (PRMCT) using in vivo mouse models and a pilot clinical trial involving patients with prostate cancer and bone metastasis. The mouse studies provided an opportunity to evaluate the impact of osteolytic and osteoblastic tumor models on the imaging metrics along with the effect of therapeutic intervention and results revealed that the multimodal approach could detect spatiotemporal tumor and bone alterations. Furthermore, results from the clinical study provide initial results showing that this approach can be successfully translated into the clinical setting allowing for multimodal disease monitoring of dynamic bone microenvironmental alterations associated with metastatic prostate cancer to the bone. Integration of the PRM imaging results into future clinical workflow will impact our ability to assess putative therapies in clinical trials as well as in routine patient management.In this present study we investigated the use of multimodal PRM imaging biomarkers for their ability to quantify dynamically changing tumor-host interactions by monitoring tumor treatment response  were evaluated in an intra-tibial model of prostate cancer metastasis to the bone using DW-MRI. Androgen independent human prostate cancer  cells we5) suspended in 10µl of media were injected into the trabecular bone of the tibial metaphysis. Sham surgeries were performed on the left leg using an identical procedure by injecting media only.All studies involving the use of mouse protocols were approved by The University of Michigan Committee on Use and Care of Animals. Male severe combined immunodeficient (SCID) mice were included in the study at 4–6 weeks of age. Mice were anesthetized by intraperitoneal injection of a mixture of ketamine (100 mg/kg) and xylazine (10 mg/kg). A Hamilton syringe with a 28-gauge needle was inserted in the middle of the patella ligament through the tibial crest epiphysis and growth plate. To prevent pain, Carprofen (5mg/kg) was injected subcutaneously. PC3 cells , IR (n = 6), docetaxel + IR (n = 6), or vehicle control (n = 8). Docetaxel treatment at a dose of 20 mg/kg in 10% DMSO was administered weekly via intraperitoneal injection for three weeks. The effect of IR on PC3 tumors was evaluated alone and in combination with docetaxel at a dose of 2 Gy/day 5 times per week for two weeks using an IC-320 Specimen Irradiation System. For combination therapy, animals were first treated with docetaxel followed by IR 4 hours later.When tumor volumes reached 7 to 15 mmMRI scans were acquired on days 0, 1, 4, 7, 11, 14, and then weekly until tumors surpassed a pre-defined terminal volume increase of 400% from initial volume. Imaging was performed using a 9.4T Agilent system. DW-MR was accomplished using a spin-echo sequence, with a navigator echo and gradient waveforms sensitive to isotropic diffusion  as previCT for the detection of bone remodeling. PC3 prostate cancer cells were cultured and implanted in mice as described above in Study 1.Bisphosphonate treatment using zoledronic acid early after tumor model induction was evaluated in a mouse model using x-ray micro-computed tomography (µCT) of the bone to assess the sensitivity of PRMThe bisphosphonate zoledronic acid was evaluated as a bone-protective agent, administered early after initiation of the bone tumor (n = 8) and compared to vehicle controls treated with PBS (n = 5). Treatments of 5mg/kg began 3 days post-implantation (day 0 for the study) and were administered twice weekly for four doses .Acquisition of µCT scans were twice weekly until study end. Images were acquired using a Siemens Inveon system (56-µm voxel size). DW-MR acquisitions were as in Study 1.Osteoblastic lesions derived from LAPC-9 cell implantation ,47were tDW-MR scans were acquired weekly until the end of the study as described in Study 1. Changes in tumor volume and ADC were monitored to evaluate tumor treatment response. Bone response was evaluated using weekly µCT imaging to detect changes in mineral density in the vicinity of the tumor.Eligible patients required a confirmed diagnosis of prostate cancer, evidence of bone metastatic disease by bone scan for which initiation of systemic therapy would be undertaken. All patients underwent standard disease imaging with bone scan and CT of abdomen and pelvis. Patients with newly diagnosed metastatic disease underwent androgen deprivation while patients with castration resistant prostate cancer (CRPC) were treated with systemic non-hormonal therapy. The study was approved by the University of Michigan Institutional Review Board and all patients signed as IRB approved consent.Image-derived metrics included volume percent change of the number of tumor voxels from registered diffusion MRI maps and CT images which were compared to clinical response outcomes to investigate the utility of PRM as an early predictor of therapy in these patients. Patients were separated into response groups based on the response criteria used in this trial where outcomes were defined by the following post-therapy PSA changes:Complete Response (CR): Undetectable PSA (<0.2 ng/ml) confirmed by another PSA level at no less than 4 weeks.Partial Response (PR): Decrease in PSA value by ≥ 50% confirmed by another PSA level at no less than 4 weeks.Stable Disease (SD): Patients who do not meet the criteria for CR, PR or PD were considered stable.Progression (PD): A 25% increase over baseline or measured nadir PSA level whichever is lower and an increase in the absolute value of PSA level by 5 ng/ml that is confirmed by another PSA level at no less than 4 weeks.Shown in Baseline MRI was performed before therapy (week 0) and again at approximately 2 and 10 weeks post-treatment initiation using a clinical Philips 3T scanner. The MRI protocol included: 3-plane anatomical survey; Proton-Density; STIR Fat-Suppressed; T1-weighted, and DW scans.Computed tomography (CT) scans consisted of pre-treatment acquisition and one additional scan collected as part of routine clinical care at approximately 10 week post-treatment initiation. Standard bone imaging protocols were used consistently for all patients, including helical acquisition and intravenous contrast.99mTc-MDP bone scans were acquired following injection of 20–25 mCi of 99mTc-MDP intravenously with imaging acquisition occurring following a 3–5 h uptake period.Bone scans were obtained pre-treatment and again at approximately 10 week post-treatment initiation as part of routing clinical care. The 3 or greater were included in the clinical study to maintain a sufficient number of voxels and minimize partial-volume effects.Tumor volumes of interest (VOI) were determined by contouring the tumor on each slice of the high-b DW-MR image and integrated across slices to provide a volume estimate. Only tumors of 4 cm1 and S2 are the signal intensities at b-values b1 and b2, respectively, and ADC1–2 is the apparent diffusion coefficient obtained using b1 = 120 and b2 = 1200 s/mm2 which desensitizes the diffusion measurement to local perfusion effects.MR data was analyzed using software developed in MATLAB. ADC maps were generated using a two-point subsampling  of the sCT images were calibrated to the Hounsfield scale using a water or water-equivalent phantom (= 0 HU) and surrounding air (= -1000 HU) on a linear scale. For mouse images, VOIs were generated to encompass the tibia from the tibia-fibula junction to the tibial plateau. VOIs for clinical images were contoured to encompass the bone surrounding the known location of the osseous lesion.The Parametric Response Map is capable of detecting changes in quantitative imaging metrics on a voxel scale ,25,39. IX+, red), decreased , or unchanged , where X denotes the quantitative index under analysis . PRM analysis was applied to both ADC values as a measure of change in tumor cellularity [2/sec was determined and for HU a threshold of 391 or 100 HU was determined to be the upper and lower limits of the respective confidence limits for mice and human patients [After registration, each image sub-volume (voxel) is associated with two quantitative indices, one pre-treatment and the other post-treatment. Image voxels were statistically classified by their change over time (ΔX) using a pre-determined threshold  into one of the following categories: increased . Significant difference between groups was determined by p < 0.05 . All data was presented as mean±SEM.Trabecular bone tumors underwent treatment initiation within 4 weeks post-cell implantation. Tumor volumes of control animals showed continual growth throughout the study until an endpoint of +400%  was reached . Animals-3 mm2/s. Control tumors exhibited little change in ADC over the first 7-day period post-treatment, as evidenced in the representative overlay .Changes in mean tumor ADC for each group showed similar magnitudes of response compared to PRMADC Fig  and 2C. CT to detect the effects of a bisphosphonate drug on slowing down bone resorption which should allow for bone-forming cells the time needed to rebuild normal bone through bone remodeling. As shown in HU+ bar plot  at day 21 compared to untreated PC3 animals .Docetaxel-treated LAPC-9 tumors were also evaluated against controls using PRM. The LAPC-9 tumor has been shown to have a significant osteoblastic effect when implanted in mice and PRM analysis revealed that there was in fact a slower and mixed PRM animals  indicatiHU analysis for this comparison was performed using a µCT image acquired the day of implantation as the baseline, compared to the previous studies which used the day that treatments were initiated (pre-treatment) as baseline. In HU+ (gain of bone density) over time appeared to be greater in animals with PC3 tumors as compared to animals with LAPC-9 tumors. However, inspection of PRM color overlays for PC3 and LAPC-9 tumors in Figs HU- values which reflect bone loss was significantly larger for untreated PC3 tumors versus animals with LAPC-9 tumors.Comparison between untreated PC3 and LAPC-9 tumor growth signatures via PRM analysis is shown in ADC measurements were monitored per individual lesion on all 12 patients, resulting in 18 lesions for the SD group and 7 for the PD group. For CT-based PRMHU measurements, 5 patients analyzed were SD (12 lesions) and 4 patients were PD (7 lesions). Representative PRM color overlays are presented in HU (left) and PRMADC (right). In these panels, blue represents regions of decreased value, red increased value, and green statistically unchanged value. Shown in HU- and PRMADC+. Early increases in tumor ADC quantified by PRMADC at 2 weeks post-treatment were positively correlated to treatment response based upon changes in PSA levels. Patients with clinically determined stable disease generally had a significantly greater volume fraction of their tumor with increased ADC values (17.2±3.9%) versus patients which were subsequently determined to have progressive disease (7.1±1.4%). Tumor-regional PRMHU- volumes quantified by PRMCT at approximately 9–12 weeks post-treatment were positively correlated to treatment response based upon changes in PSA levels. Patients with stable disease also generally had a significantly greater volume fraction of their tumor-regional VOI with reduced attenuation (13.2±4.2%) versus patients with progressive disease (2.5±0.6%).In the pilot clinical trial, patients with CRPC underwent both DW-MRI and CT scanning protocols. Of the 12 patients 8 were clinically determined to have stable disease (SD) and 4 to have disease progression (PD) by standard criteria at first disease assessment. DW-MRI PRM99mTc-labelled bisphosphonates is widely used to stage patients with bone metastasis [99mTc-labelled bisphosphonates by metastatic lesions may occur after initiation of systemic hormone or chemotherapy and is refered to as the flare phenomenon [Skeletal involvement remains the leading cause of morbidity and mortality in patients with prostate cancer. Bone scintigraphy with tastasis . The abitastasis . Quantifenomenon . This phThe overall goal of this research study was to evaluate the voxel-based PRM biomarker for quantification of multi-modal images to assess the pathology and treatment effects of multiple tissues including tumor and stroma in the context of metastatic prostate cancer to the bone. Pathological correlation of loss of tumor cell density with increasing ADC values by DW-MR as well as loss of bone mineral density with decreasing in HU values in CT scans have been previously reported by our group ,43. CompThe objective of this study was to evaluate DW-MRI and CT as imaging biomarkers for quantification of growth progression and treatment response associated with changes in tumor cellularity (DW-MRI) and bone density (CT), respectively in metastatic cancer to the bone. We explored the capabilities of PRM voxel-based analysis of MRI and CT images to quantify and display changes in both tumor tissue and the bone stromal compartment in response to metastasis and treatment intervention. The hypothesis was that PRM may be useful for analyzing DW-MRI scans to provide for an early response biomarker and similiarly that PRM analysis of CT scans would provide insights on the impact of the tumor involvement and treatment on the stromal compartment.ADC biomarker revealed that at day 7 post-treatment, a significant increase in tumor ADC values had occurred in the single therapy groups which were also found to be even higher in the combination treatment group  was found to be greater in the SD group versus the PD patient group. This may indicate bone re-normalization in patients who responded to therapy. Although lesion phenotyping was not performed in this study, regions of the metastases generally seemed to appear sclerotic on CT images, which agree with the hypothesis that a decrease in attenuation in these regions may indicate progress toward a normal state and positive treatment outcome. The use of PRMHU in the context of monitoring changes due to bone metastases and treatment intervention appears promising and further studies based upon these novel findings are warranted. Moreover, DW-MRI scans (PRMADC) at 2 weeks post-treatment revealed that early increases in tumor ADC values could be detected which were positively correlated to a subsequent positive clinical response. The fact that patients with SD were shown to have a significantly greater volume fraction of their tumors with increased ADC values versus PD patients also reveals the potential for using PRMADC as an imaging biomarker for early treatment stratification of patients with metastatic prostate cancer to the bone. These results support earlier findings in hormone naïve patients which showed similar increased PRMADC values in therapeutically responsive patients [In the pilot clinical trial, we used both DW-MRI and CT scanning protocols to evaluate their ability to detect changes in patients with CRPC. While patient numbers were relatively small, the volume fraction of the tissue that had a significant decrease in HU value  interactions which were specific to overall treatment outcome with similar findings in both the mouse and human studies. Further development and evaluation of DW-MRI and methods for quantification and visualization of response such as PRM as a noninvasive quantitative imaging biomarker are warranted to more fully explore the clinical impact of this approach.S1 CONSORT Checklist(PDF)Click here for additional data file.S1 Protocol(PDF)Click here for additional data file.