A new view for protein turnover in the brain

Lysosomes were identified in dendrites and dendritic spines using various techniques. Cultured neurons show lysosomes throughout neurons and in dendritic spines indicated in yellow (left and upper right); brain slices show a lysosome in the …more

Keeping the human brain in a healthy state requires a delicate balance between the generation of new cellular material and the destruction of old. Specialized structures known as lysosomes, found in nearly every cell in your body, help carry out this continuous turnover by digesting material that is too old or no longer useful.

Scientists have a strong interest in this degradation process since it must be tightly regulated to ensure healthy brain functioning for learning and memory. When lysosomes fail to do their job, brain-related disorders such as Parkinson’s and Alzheimer’s are possible.

Scientists at the University of California San Diego, led by graduate student Marisa Goo under the guidance of Professor Gentry Patrick, have provided the first evidence that lysosomes can travel to distant parts of neurons to branch-like areas known as dendrites. Surprisingly, they also found that lysosomes can be recruited to dendritic spines, specific areas where neurons communicate with each other. The researchers also revealed that direct activation of a single dendritic spine can directly recruit lysosomes to these specialized locations. The results are published in the Aug. 7 issue of the Journal of Cell Biology.

“Previously there was no reason to think that lysosomes could travel out to the ends of dendrites at synapses,” said Patrick a professor of neurobiology in UC San Diego’s Division of Biological Sciences. “We are showing neuronal activity is delivering them to the synapse and they are playing an integral and instructive role in remodeling and plasticity, which are so important for learning and memory.”

The researchers used genetically encoded fluorescent markers to label lysosomes and follow their movements, sometimes tens and even hundreds of microns away from the cell body. Confocal, two-photon, and electron microscopy were used to reveal that lysosomes move in dendrites and are present in spines, something previously unseen.

“We’ve shown that lysosomes can be recruited to a single synapse… until now we had no idea that lysosomes could receive such instructive cues,” said Patrick, “For many neurodegenerative diseases, disfunction seems to play a role. So now we can look at the distribution and trafficking of lysosomes—which we now know are controlled by neurons—and ask: Is that altered in disease?”


Photosynthesis by marine algae produces sound, contributing to the daytime soundscape on coral reefs


We have observed that marine macroalgae produce sound during photosynthesis. The resultant soundscapes correlate with benthic macroalgal cover across shallow Hawaiian coral reefs during the day, despite the presence of other biological noise. Likely ubiquitous but previously overlooked, this source of ambient biological noise in the coastal ocean is driven by local supersaturation of oxygen near the surface of macroalgal filaments, and the resultant formation and release of oxygen-containing bubbles into the water column. During release, relaxation of the bubble to a spherical shape creates a monopole sound source that ‘rings’ at the Minnaert frequency. Many such bubbles create a large, distributed sound source over the sea floor. Reef soundscapes contain vast quantities of biological information, making passive acoustic ecosystem evaluation a tantalizing prospect if the sources are known. Our observations introduce the possibility of a general, volumetrically integrative, noninvasive, rapid and remote technique for evaluating algal abundance and rates of primary productivity in littoral aquatic communities. Increased algal cover is one of the strongest indicators for coral reef ecosystem stress. Visually determining variations in algal abundance is a time-consuming and expensive process. This technique could therefore provide a valuable tool for ecosystem management but also for industrial monitoring of primary production, such as in algae-based biofuel synthesis.


Coral reef degradation and algal smothering are caused by human impacts such as overfishing, pollution through nutrient runoff and climate change effects. Algal dominance diminishes the value of reefs [1], is likely permanent [2, 3], and serves as a clear indicator of ecosystem degradation [4]. Timely monitoring of reef state is crucial to quantifying impacts and mitigating ecosystem shifts [5], but present-day assessment methods are resource intensive and infrequent [6]. Because coral reefs are highly soniferous ecosystems where most sound comes from biological processes, ecologists have sought to apply the integrative acoustic monitoring approaches used successfully in terrestrial soundscape ecology [7, 8]. These techniques can yield spatially integrative and long-term continuous observations that can be collected autonomously with inexpensive recorders, but the sources of many reef sounds are challenging to identify [9]. Previously we established positive correlations (Pearsons ρ) between benthic macroalgal cover and aspects of coral reef soundscapes (acoustic pressure spectral density levels in the 2 to 20 kHz band) at 17 sites over a 2570 km transect from Kure Atoll to the Island of Hawaii [10]. These correlations were only observed during the day when overall sound levels were typically lower (Table 1), and correlations of this nature did not exist between sound and any other non-acoustic environmental metric. To further deconvolute the soundscape sources, we performed tank-based passive acoustic experiments with the invasive Hawaiian algae Salicornia gracilaria. Ambient sound was recorded in combination with dissolved oxygen and time-lapse photography of algae under daylight and night conditions to investigate the mechanism behind the observed associations of sound with algal presence.


Table 1. Correlation between benthic algal cover and coral reef soundscapes.

Pearson’s correlation coefficients between the percentage of benthic macroalgal cover at 17 reef sites throughout the Hawaiian Islands and ambient acoustic pressure spectral density in three bands between 2 and 20 kHz, obtained simultaneously at the same locations. Intensity-filtered spectra were averaged over one hour beginning at the indicated times.


Short-time Fourier analysis on intensity filtered soundscape recordings [10] provide a more spectrally detailed view of individual transient soundscape components (i.e., most reef-based biological sound). Typical Fourier analysis approaches in underwater acoustics integrate over time periods that are much longer than each individual biological sound. While longer integration results in increased frequency resolution, ideal for detecting tonal and/or narrow-band sources, the approach can also spectrally smear multiple transient sounds together and/or reduce peak level estimates through giving equal weighting to quiet periods between transient arrivals. If a persistent environmental noise is present, it can mask some transient biological sounds sampled through this approach. Our approach involved selecting each transient using an intensity filter, ensuring the transform length encompassed only the transient, and then assessing the spectral qualities of each transient individually. As a consequence, reef spectra shown here cannot be directly compared to spectra obtained without intensity filtering. Depending on ambient and preamplifier noise levels, lower-level sounds from individual biological sources may not be discernable at significant distances from the source. However, many of these events occurring over a distributed area create an extended sound source for which propagation models show decreased attenuation with distance [11].


With the onset of photosynthesis during light periods, bubbles could readily be observed with the naked eye on the surface of macroalgae (Fig 1A and 1B). As the bubbles detached from the plant, they created a short ‘ping’ sound. Acoustic recordings of bubble release separated by quiet periods consequently appear as an irregular pulse-train-like time series (Fig 1C). The sound created by perturbation of the bubble during release is naturally transient and decays exponentially (Fig 1D). Oversampled recordings (100 kS·s-1) permit more detailed spectral analysis of each sound (Fig 1E) and fundamental frequency estimates for each bubble can be made from the resultant data (Fig 1F). Tank resonance modes [12] were restricted through the presence of algae and the drafted, acoustically absorptive nature of the tank walls and did not appear to substantially influence the recorded spectra (Fig 1G).


Fig 1. Characterization of bubble sound from algae.

(A) Gracilaria salicornia actively creating gas bubbles during photosynthesis. White arrows indicate the locations of larger bubbles. The scale bar is 5 mm in length (B) A closer view of one gas bubble about to detach from the algae surface. The scale bar is 2 mm in length. (C) A 9 s time series of ambient sound from inside the aquarium. (D) A high-resolution view of a typical transient waveform as shown in (C). (E) Spectrogram showing the time-varying spectral content of the received waveform in (D). (F) Pressure spectral density (PSD) estimates showing mean, median and maximum pressure spectral densities of received level from the 2 ms period as shown in (D) and (E), indicating the spectral peak of the waveform as 13.07 kHz (vertical dashed line). (G) An averaged spectrum from transient sounds recorded over one hour overlaid with standing-wave resonant tank modes. Mode sums indicate the sum of mode numbers in the horizontal (length, width) and vertical (depth) directions.


Experiments with S. gracilaria in controlled laboratory settings demonstrate that sounds produced by algal photosynthesis are similar in nature to components of the soundscape recorded in shallow water regions where S. gracilaria and other benthic macroalgae species are common. With the intermittent application of Photosynthetically Active Radiation (PAR, μ = 381, σ = 67 μmol·m-2s-1, roughly equivalent to the available level at water depths of 10 m on a cloudy day in the equatorial pacific) the link between photosynthesis, dissolved oxygen and acoustic emissions through bubble formation becomes apparent (Fig 2 and S1 Fig). Over the duration of PAR application both the number of bubbles (R2 = 0.76, S2A Fig) and their mean size (R2 = 0.47, S2B Fig) increased with dissolved oxygen levels. The process was reversed with the removal of light. These increases in the size and rate of bubble formation during the illuminated period lower the frequency distribution of sounds produced by the algae (Fig 2A) and cause an increase in the Sound Exposure Level (SEL, R2 = 0.82, Fig 2B and S2C Fig), a measure analogous to acoustic work. The onset of bubbling rate and size increase from the application of light was delayed due to the time required for photosynthesizing algae to build supersaturation conditions in the surrounding water, and for bubbles to reach a size large enough to create sufficient buoyancy force to detach from the algae. After removal of the light source there was a comparable delay in the reduction of bubble production and sound, due to bubbles remaining on the algae and saturation state remaining above 100 percent.


Fig 2. The acoustic response of algae to light over time.

(A) Ten-minute averaged time-frequency histograms showing the distribution of Minnaert frequencies from bubbles produced by algae with the application and removal of PAR. The color scale indicates the number of bubble events acoustically characterized over a 10-min period, per frequency bin (195 Hz bin width). (B) Sound Exposure Level (SEL, 10 min. averages, ± 1 S.E., left axis) and dissolved oxygen concentration (right axis) showing an increase, decrease, and increase of dissolved oxygen and SEL with the application, removal and application of PAR, respectively. The grey regions indicate the period when the light source was removed.


A comparison between acoustically derived bubble size distributions and photographically obtained measurements of bubble size (Fig 3) optically validates the acoustic estimation of algae-driven bubble radii (99 percent significance: no evidence the distributions were unequal, Wilcoxon 2-sided test, p = 0.9087).


Fig 3. Size distribution comparisons between bubble radii distributions simultaneously derived through photography and passive acoustic recording.

(A) Bubble size distributions obtained from photographically imaged measurements (histogram, left axis) and Minnaert radii (solid line, right axis) every 150 s over a one-hour period of active bubble formation. (B) Boxplots indicating 25th, 50th, 75th percentiles and 5th and 95th percentile outliers (crosses) of optically imaged and acoustically derived bubble radii distributions over the same period.



Algae release oxygen as a byproduct of photosynthesis. While these waste molecules are formed intracellularly in solution, the nucleation of oxygen gas bubbles on the surface of macroalgal tissue takes place when localized supersaturation of dissolved oxygen occurs at a nucleation site. Depending on the timescale of bubble formation and the total gas tension, the diffusion of nitrogen will also contribute to total bubble volume. Previous work has shown the relationship between oxygen bubble production by algae and oxygen supersaturation at their surface microenvironment [13]. The bubbles grow with the addition of more waste oxygen produced by photosynthesis and nitrogen diffusion, ultimately separating from the algae through a combination of buoyancy and surface tension forces. As a spherical bubble is perturbed through release, it oscillates in volume and pressure with exponential decay at a frequency inversely proportional to its radius R0, the roots of the specific heat ratio of the gas γ and ambient fluid pressure Pfl, and the inverse root of ambient fluid density, ρfl, a relationship first derived by Minnaert [14] and now referred to as the Minnaert frequency [15] ωMinn: (1)

This approximation was first developed with the assumption of negligible heat flow (adiabatic conditions) and negligible surface tension, but has since been shown to be a good approximation for bubbles of radii between 30 nm to 300 μm. Consequently, passive acoustic estimates of bubble volume can be made if the water properties and depth at which bubble separation occurs are known.

Our observations demonstrate the mechanism behind previously established correlations between components of underwater soundscapes and the relative abundance of macroalgae on Hawaiian coral reefs [10]. Although ecologically distinct, bubble production is ubiquitous in sea grass beds [16], and can also be observed in marine algal species [17]. Thus, the bubble production mechanism is not specific to S. gracilaria and may be used as a general indicator of photosynthetic activity. Ultrasonic emissions from terrestrial plants are also driven by bubble processes, but the underlying mechanism is water starvation rather than photosynthesis [18].

The contribution of algal sound to soundscapes may be isolated from other ubiquitous sound sources though matched filtering, directional receivers and similar techniques. The observed size distribution and thus corresponding spectral distribution of bubble sound (Fig 3A), and the a-periodic and un-clustered nature of acoustic emission time series (Fig 1), are not shared by any other bubble related process in shallow-water environments, including breaking waves [19]. While active acoustic techniques have been used to estimate marine plant biomass [16, 20], our finding introduces the possibility of noninvasive sensing methods that can quantify relative primary production rates in addition to presence/absence, although further work in disentangling confounding factors is necessary. The relationship between bubble formation processes and gas transfer mechanisms has been well studied [13, 2122] providing a background for the application of bubble science to underwater ecological sensing.

The findings presented here show that algae are capable of producing sound under normal circumstances and that sound may explain a correlative association previously discovered between sound in the 2 to 20 kHz band and algal cover on coral reefs [10]. However, a great deal of further work is required before a passive acoustic tool could be developed for quantifying bubble output from photosynthesis, in-situ primary productivity and algal abundance. A number of caveats in our analysis must be considered and results from soundscape correlation and tank experiments should be considered limited in scope until these caveats are resolved. Underwater soundscapes, especially in the vicinity of a shallow coral reef, are extremely complicated acoustic environments containing sound sources of many types [23]. A number of spatio-temporally variable physical mechanisms that influence the production and propagation of sound from source to receiver also exist.

The most notable interference may be snapping shrimp noise. While the sound generation mechanism of these shrimp involves cavitation [24] and is thus spectrally unique from the relatively narrow-band bubble emissions discussed here, source levels and rate of occurrence can be so high [2527] that they are capable of ‘drowning out’ other sound sources for protracted periods of time. Furthermore, the spectral, temporal frequency, and diurnal behavior of snapping shrimp vary depending on the location and time of year [2829, 2627]. While the frequencies produced by photosynthetic bubbles are within the band of sound produced by snapping shrimp, spectral analysis of sound from shrimp-dominated and coral reef soundscapes reveal spectral differences that suggest other biological contributors can be spectrally evaluated even in the presence of shrimp noise. Bubbles produce transient sounds that contain energy in a relatively narrow frequency band. Due to the higher likelihood of smaller bubbles, it is less likely that bubbles would add significant energy at lower frequencies (i.e., below 5 kHz). Conversely, sound produced by snapping shrimp is broad-band but weighted toward a peak between 4 to 6 kHz [25]. The integration of snapping shrimp sounds over time can produce a low-frequency-weighted spectrum with monotonic decay. Conversely, some reef soundscapes may create higher-frequency-weighted spectra that may include distinct spectral peaks (Fig 4A). Limited comparisons between a snapping-shrimp-dominated soundscape and those correlated with algal dominance in Table 1 show a variation in band levels between 5 and 20 kHz between sites. In this case, the shrimp-dominated soundscape is the Scripps Pier in La Jolla, Ca. Periodic cleaning of encrusting sponges on the pilings reveals large communities of snapping shrimp. Care should be taken when evaluating this comparison as the behavior and sound of snapping shrimp may vary between Hawaii and San Diego. However, the Scripps Pier is relatively unique in that no reefs exist within half a mile of the pier. Recordings were made on a calm day with little swell. The comparison shows that in daytime reef soundscape levels were between 5 to 12 dB higher above approximately 5 kHz. Barring differences in frequency-dependent attenuation or shrimp sound characteristics, the comparison suggests an additional source of higher frequency sound present at the reef sites (Fig 4B). The high source levels emitted by snapping shrimp drive the prevailing thought that they are the overwhelmingly dominant bioacoustic source in coastal underwater ecosystems. However, our suggestion that individually quieter biological sources of sound are detectable in the presence of snapping shrimp is not unprecedented. Chorusing from sea urchin grazing is known to contribute to soundscape spectra off the coast of New Zealand [30] and hermit crabs have been spectrally matched to soundscapes from environments in which both they and snapping shrimp are plentiful [9]. Nevertheless, before accurate quantification of algal photosynthetic activity can be attempted in the field, the behavior and acoustic characteristics of snapping shrimp in the area must be understood to create validated acoustic signal processing algorithms capable of differentiating between shrimp noise and algal sound.


Fig 4. Spectral comparisons between Hawaiian reef soundscapes and the snapping-shrimp dominated soundscape at the Scripps Pier in La Jolla, CA.

(A) Normalized, intensity filtered pressure spectral density levels recorded at midday local time during sunny days on shallow Hawaiian reefs (colored lines) plotted with similarly filtered levels obtained during crepuscular chorusing of snapping shrimp communities on the Scripps pier (black line with grey shading underneath). (B) Logarithmic spectral level differences between crepuscular pier and midday Hawaiian reef soundscapes in Fig 4A. Note that 3 dB represents a doubling or halving of the spectral level difference in each frequency bin. Hawaii locations are identified as follows: FFS–French Frigate Shoals; Kure–Kure Atoll; Big I.–Ke’ei Beach, Big Island Hawaii; Lis.–Lisianski Island; Oahu–Lai’e Beach, Oahu, Hawaii; Maui–La Perouse Bay, Maui, Hawaii; PHR–Pearl and Hermes Reef; Kauai–Tunnels Beach, Kauai, Hawaii.


Another confounding factor may be the influence of water movement on bubble formation and retention. Bubbles will not form if water is continually swept from the algae and oxygen saturation state never exceeds 100 percent. Secondly, if bubbles are formed, they may be prematurely removed through wave action, decreasing the size distribution of the bubbles and shifting the frequency distribution higher. The mechanisms governing benthic oxygen saturation state and the effects of water movement on bubble retention are poorly understood. Benthic algae reside at least partially within the fluid boundary layer, especially in rugose environments, and may be somewhat protected from flow. The structure of algal filaments may also assist or retard the removal of bubbles by wave action, meaning that aspects of sound production are likely species-specific. While bubble retainment by algae in current remains to be quantitatively investigated, Fig 5 shows typical turf algae in a shallow reef environment off Hawaii’s Big Island on a sunny day. Fig 5 demonstrates that in very shallow water subject to wave action, bubbles from photosynthesis may be retained if sufficient algal structure exists. Further acoustic, flow, and bubble physics analysis, along with an investigation of biological interaction (both floral and faunal) are required to quantify and understand the impact water movement has on bubble formation and the consequent acoustic emissions by algae in these scenarios.


Fig 5. Bubbles from photosynthesis present on shallow algal turf subject to swell.

Note the movement of suspended particles during the camera exposure period of 1/125 sec. Scale bar indicates 2 cm. Image courtesy Florybeth La Valle, University of Hawaii.


The probability that too many transient sounds arrive at the receiver during one FFT integration period for spectral analysis on individual sounds to be performed increases with the size of the reef, the nature of propagation pathways from sources to the receiver, and the acoustic productivity of the benthic community. As the range between any given source and the receiver increases, the probability that multipath arrivals are interpreted as separate signals also rises. However, the spectral content of multipath arrivals is less likely to differ significantly from the direct arrival when compared to other sound sources. Future data collection with a spatial filter such as a directional receiver or hydrophone array can limit the area over which passive acoustic surveys are performed, simplifying the challenges associated with multipath and the large number of sources.

Sound is the most efficient radiation underwater and we continue to discover that important biological components of many aquatic ecosystems are both sensitive to [31] and produce acoustic emissions. Contributions to biological soundscapes on coral reefs include sound deliberately produced by fishes and invertebrates for communication [32] and defense [25], or inadvertently through feeding [30] and movement [9]. To date these sounds have been used to study the behavior and distribution of marine mammals and fish [3233], to understand ambient noise levels in the ocean [3435], and to assess the impacts of anthropogenic noise on marine environments [36]. Soundscape measurements are also effective at night when benthic activity is greatest [9] and passive optical techniques are difficult or impossible to implement [37]. In concert with these acoustic observations, a volumetrically integrative, remote and noninvasive method of measuring primary productivity outputs may lead to more accurate, persistent and less expensive surveys of ecological state in shallow water, the majority of which are currently performed in-situ through diver-based optical methods. Quantifying primary productivity through passive acoustic monitoring could become a technique that may not be limited to coral reef environments. Macroalgae are the dominant benthic primary producer in many temperate coastal and freshwater environments that are more difficult to survey optically due to reduced water clarity. Passive acoustic monitoring of ebullition has been utilized in the monitoring of chemical reactions [38]. A semi-real-time and volumetrically integrative approach can be adopted in biofuel-generating algal reactors using similar methods. The sensing technique proposed here is restricted neither to in-situ observations nor to marine environments but may apply basically to all aquatic environments in which macroalgae grow.

Materials and methods

Background data

The summary data shown in Table 1 were collected in the Northwest Hawaiian Islands (NWHI) during a NOAA Remote Areas Monitoring Program Cruise HA-12-04 on the R/V Hi’Ialakai during August 2012 and from sites accessible from shore on the Main Hawaiian Islands during September-October 2012. Permits were obtained for conducting research in the Papahānaumokuākea Marine National Monument (PMNM-2012-029) and the Main Hawaiian Islands (Department of Land and Natural Resources special activity permit 2012–83). Acoustic data were collected using a single hydrophone Loggerhead Instruments DSG Ocean recorder configured with a sampling frequency of 80 kSs-1 and an electronic gain of 20 dB. Simultaneous with acoustic recorder deployments, benthic phototransects were taken in accordance with NOAA Coral Reef Ecosystem Division rapid ecological assessment protocol [39]. Pearson’s correlation coefficient, ρ, between all phototransect-derived environmental variables, and band-limited pressure spectral density estimates, were calculated over data from all field sites for which acoustic and phototransect data were available. Metrics for which ρ values were greater than 0.6 with p < 0.001 (Bonferroni correction) were considered to be sufficiently correlated. Correlations of particular interest were those that appeared to form a distinct pattern across several metrics (i.e., similarly high correlation across adjacent acoustic bands, combined with a consistent temporal pattern). A full description of the data collection and processing methods associated with Table 1 is available in the associated publication [10].

Summary of the experiment

Tank-based experiments were carried out at the Hawaii Institute of Marine Biology at Coconut Island, Kaneohe Bay, Hawaii. Ten kilograms (wet) of the invasive red algae Gracilaria salicornia were collected by the Department of Aquatic Resources (DAR) of the State of Hawaii in Kaneohe Bay and stored in a 1 m diameter seawater holding tank with a steady filtered (100 um) ocean water flow. One kilogram of algae was visually inspected to remove any associated fauna and relocated to a smaller, plastic, opaque-sided rectangular tank with internal dimensions of 550 x 300 x 300 mm. Seawater was used to fill the tank to a depth of 250 mm and a 50 mm layer of algae was positioned on the floor of the tank between 200 and 250 mm depth. The algae remained negatively buoyant throughout the experiment. An aquarium light (Radion XR30w G4 pro) was positioned centrally over the tank and 228 mm (9 in.) above the water surface. All testing was conducted in a dark room with the aquarium light being the only source of photosynthetically available radiation (PAR). The color temperature was set at close to that of natural sunlight on a clear day (5500 K), at which the light operated at 55 percent of maximum output. Software limitations required a warm-up and cool-down period of 30 min before and after full-strength application, during which light levels were linearly ramped up and down between zero and 55 percent. Mean PAR level was obtained from measurements at fifteen equally spaced locations at algae equivalent depth using a calibrated LI-COR Li-193 spherical underwater quantum sensor and a LI-COR Li-250 light meter. Note that the mean PAR level obtained (381 μmol·m-2s-1) is far below what is expected near the surface on a calm sunny day in the tropics [40]. The experiment was started with algae being brought out of a 24-hour period of darkness and exposed to PAR as described above. After 3 h and 45 min, light levels were reduced to zero. After 7 h of darkness, light levels were increased back to the levels described above until the conclusion of the experiment after 10.5 h of light. The algae were then removed from the tank and disposed of in accordance with DAR procedure.

Acoustic recordings

Acoustic data were collected with a High Technology Inc. HTI-92-WB hydrophone, equipped with a low-power, high sensitivity preamplifier. Hydrophone and preamp system sensitivity was -144.8 dB re 1 VμPa-1 over the 2 Hz to 40 kHz band. The hydrophone was positioned to one side of the tank at 125 mm depth, 60 mm from the nearest tank wall. The preamplifier was powered by a variable DC power supply set at 10 V and drawing 2 mA. Data were acquired using a National Instruments® USB-6366 data acquisition module connected to a laptop computer running National Instruments® LabView® 2013 (SP1) and customized script. Time-stamped data were sampled at 100 KSs-1 at 16-bit depth in discrete 10 s intervals, pausing for a short time in between to save the data and verify that the sampling frequency was accurate. The recordings were processed in Mathworks Matlab® software. Before analysis, a high-pass, 5th order Butterworth filter (stop band: 0–0.5 kHz, pass band: 0.8–50 kHz was applied to remove DC offset, 60 Hz electrical noise or low frequency noise from passing vehicles and other sources.

Acoustic data processing

The peaks in amplitude associated with bubble separation ‘pings’ were identified from pre-filtered acoustic data if absolute peak amplitudes were greater than 5 σ from preamp self-noise levels. A 10 ms window around each of these peaks was isolated for further analysis. Information regarding the number of qualifying peaks and the temporal distribution of inter-peak periods within each 10 s file was retained. In order to calculate the sound exposure level (SEL) of bubble sound, the time duration of each air bubble “ping” was determined as the time between the 5th and 95th percentiles of the cumulative energy within the 10 ms window. SEL values were averaged over successive 10-min periods. Spectral analysis was performed on the time series between the 5th and 95th percentiles using a Kaiser-Bessel-windowed (β = 2.5π) 512-point FFT, overlapped 90 percent, meaning a bin width of 195.3 Hz and an overlapped spectral estimate made every 512 μs. The frequency bin containing the highest pressure spectral density level above 1 kHz, temporally matched to the amplitude peak in the corresponding time series, was determined to best represent the dominant frequency of each bubble separation ping. Regression of log-linear relationships and calculation of residuals was conducted using a simple logarithmic (base ten) model for the relationship between SEL and dissolved oxygen, and a natural logarithmic model for the relationship between the number and mean size of bubbles and dissolved oxygen. Residuals were calculated using linearized values.

Determination of bubble size

Estimates of bubble radii (R0) corresponding to each peak frequency were made using the Minnaert equation (1). For R0 to be a good approximation for the true radii of a freely oscillating bubble, the bubble radius must satisfy the following conditions: that the bubbles are “acoustically small”, the geometric mean of bubble radius and acoustic wavelength is large compared to the viscous boundary layer, the bubbles are “thermally large”, and the Laplace pressure is much less than the equilibrium pressure in the liquid (20).

Dissolved oxygen, pH conductivity, and temperature data

Dissolved oxygen (DO), pH, conductivity, and temperature were measured in 60 s intervals over the duration of the experiment. All non-acoustic sensors were part of a Manta+ 20 multi-probe system placed inside the experiment tank opposite the hydrophone. DO was assessed with an optical dissolved oxygen sensor provided by the Hamilton company (HDO) with a resolution of 0.01 mgL-1 or 0.1% saturation. pH was assessed via an electrolyte filled glass sensor next to a reference electrode. For conductivity, a four electrodes sensor was used while the temperature was assessed with a thermistor. Additional specifics are provided by the manufacturer [41]. While essential for the experiment, the oxygen sensor produced two types of acoustic noise that were additionally filtered from the acoustic data before analysis. The first type of noise was a startup transient that occurred upon system power-up every 60 s. A matched filter was used to exclude 0.2 s portions of recordings in which the transient was present. Secondly, a steady-state doubling of high frequency noise levels was emitted by the sensor for approximately 8 s after the startup transient. These recording periods were identified using a Hilbert-transform-based threshold detection algorithm and excluded from analysis.

Optical validation

Bubble radii were directly measured from high resolution photographs taken using a Canon SL1 single lens reflex camera equipped with a Canon 100 mm F2.8 L macro lens (160 mm effective) mounted on a tripod outside of the aquarium and aimed inward through a transparent window. A scale rule bar was inserted into the aquarium and placed across the field of view, orthogonal to the lens axis and within the depth of field. Only bubbles that were in focus within the depth of field of each image were analyzed. Photographs were taken every 150 s for one hour during a period when algae were observed to be actively producing bubbles. Radii measurements were obtained in post-processing using ENVI software, measured by pixel width and converted to μm using the scale bar in individual photographs.

Supporting information



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Chemical and acoustic responses of algae to changes in photosynthetically available radiation (PAR).

All subfigures are time-aligned. (A) Dissolved oxygen time series. (B) Acoustically determined bubble counts per minute. (C) Acoustically determined mean bubble radius per minute.


S1 Fig. Chemical and acoustic responses of algae to changes in photosynthetically available radiation (PAR).

All subfigures are time-aligned. (A) Dissolved oxygen time series. (B) Acoustically determined bubble counts per minute. (C) Acoustically determined mean bubble radius per minute.



S2 Fig. Log-linear regression plots of acoustic emissions from photosynthesizing algae.

(A) No. of bubbles per minute against dissolved oxygen levels (R2 = 0.76, exponential coefficients α = -2.54, β = 0.43). (B) Mean bubble radii per minute against dissolved oxygen levels (R2 = 0.47, exponential coefficients α = -3.85, β = 0.19). (C) 10-minute Sound Exposure Level against dissolved oxygen levels (R2 = 0.82, exponential coefficients α = 83.15, β = 1.39). The coefficients may be applied to an exponential regression of linear parameters x and y as follows: (2)




We thank G. B. Deane, F. L. Rohwer, B. Bailey and R. F. Keeling for helpful comments and suggestions; K. Lubarsky, B. Neilson, D. Lager, K. Fuller, and K. Tucker for providing algae samples; J. Davidson and W. Au for providing laboratory facilities; S. Calhoun and B. T. Reyes for calibration of laboratory equipment; F. La Valle for the image in Fig 5; J. Tomlinson, P. D. Franck and S. M. Franck for experiment logistical support; G. J. Orris, S. Ackelson, R. D. Norris, M. J. Buckingham and A. J. Miller for funding support. This work was supported by NSF IGERT grant no. 0903551, U.S. Naval Research Laboratory section 219 program funds, National Research Council and American Society for Engineering Education postdoctoral fellowships and NASA grant no. NASA-14HYSP14-0003. Two anonymous reviewers provided comments that greatly improved the quality of the manuscript.


  1. 1. Haas AF, Guibert M, Foerschner A, Calhoun S, George E, Hatay M, et al. Can we measure beauty? Computational evaluation of coral reef aesthetics. PeerJ. 2015;3: pe1390. pmid:26587350
  2. 2. Scheffer M, Carpenter S, Foley JA, Folke C, Walker B. Catastrophic shifts in ecosystems. Nature. 2001;413 6856: 591–596.
  3. 3. Jackson JBC. Ecological extinction and evolution in the brave new ocean. Proc Nat Acad Sci. 2008;105: Supplement 11458–11465.
  4. 4. Smith JE, Shaw M, Edwards RA, Obura D, Pantos O, Sala E et al. Indirect effects of algae on coral: algae‐mediated, microbe‐induced coral mortality. Ecol Lett. 2006;9(7): 835–845. pmid:16796574
  5. 5. Pandolfi JM, Jackson JBC, Baron N, Bradbury RH, Guzman HM, Hughes TP et al. Are US coral reefs on the slippery slope to slime? Science. 2005;307(5716): 1725–1726. pmid:15774744
  6. 6. Vargas-Angel B,White D, Storlazzi C, Callender T, Maurin P. Baseline assessments for coral reef community structure and demographics on West Maui National Oceanic and Atmospheric Administration; 2017.
  7. 7. Sueur J, Pavoine S, Hamerlynck O, Duvail S. Rapid acoustic survey for biodiversity appraisal. PLoS ONE. 2008;3(12): e4065. pmid:19115006
  8. 8. Kennedy EV, Holderied MW, Mair JM, Guzman HM, Simpson SD. Spatial patterns in reef-generated noise relate to habitats and communities: evidence from a Panamanian case study. J Exp Mar Biol Ecol. 2010;395(1): 85–92.
  9. 9. Freeman SE, Rohwer FL, D’Spain GL, Friedlander AM, Gregg AK, Sandin SA et al. The origins of ambient biological sound from coral reef ecosystems in the Line Islands archipelago. J Acoust Soc Am. 2014;135: 1775–1788. pmid:25234977
  10. 10. Freeman LA, Freeman SE. Rapidly obtained ecological indicators in coral reef soundscapes. Mar Ecol Prog Ser.2016;501: 69–82.
  11. 11. Radford CA, Tindle CT, Montgomery JC, Jeffs AG. Modeling a reef as an extended sound source increases the predicted range at which reef noise may be heard by fish larvae. Mar Ecol Prog Ser. 2011;428; 167–174.
  12. 12. Akamatsu T, Okumura T, Novarini N Yan HY. Empirical refinements applicable to the recording of fish sounds in small tanks. J Acoust Soc Am. 2002;112(6); 3073–3082. pmid:12509030
  13. 13. Kraines S, Suzuki Y, Yamada K Komiyama H. Separating biological and physical changes in dissolved Oxygen concentration in a coral reef. Limnology and Oceanography. 1996;41(8): 1790–1799.
  14. 14. Minneart M. XVI On musical air-bubbles and the sounds of running water. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science 1933;16(104): 235–248.
  15. 15. Leighton TG. The acoustic bubble ISBN: 0-12-44190-8; 1994.
  16. 16. Wilson CJ, Wilson PS Dunton KH. An acoustic investigation of seagrass photosynthesis. Marine Biology. 2012;159(10); 2311–2322.
  17. 17. Medwin H. In situ acoustic measurements of bubble populations in coastal ocean waters. J Geophys Res.1970;75(3): 599–611.
  18. 18. Tyree MT, Sperry JS. Vulnerability of xylem to cavitation and embolism. Ann Rev Plant Biol. 1989;40; 19–38.
  19. 19. Deane GB, Stokes MD. Scale dependence of bubble creation mechanisms in breaking waves. Nature. 2002;418(6900); 839–844. pmid:12192401
  20. 20. Wilson PS, Dunton KH. Laboratory investigation of the acoustic response of seagrass tissue in the frequency band 05–25 kHz. J Acoust Soc Am. 2009;125(4): 1951–1959. pmid:19354371
  21. 21. Ainslie MA, Leighton TG. Review of scattering and extinction cross-sections, damping factors, and resonance frequencies of a spherical gas bubble. J Acoust Soc Am. 2011;130(5): 3184–3208. pmid:22087992
  22. 22. Keeling RF. On the role of large bubbles in air-sea gas exchange and supersaturation in the ocean. J Mar Res. 1993;51(2): 237–271.
  23. 23. Freeman SE, Buckingham MJ, Freeman LA, Lammers MO, D’Spain GL. Cross-correlation, triangulation, and curved-wavefront focusing of coral reef sound using a bi-linear hydrophone array. The Journal of the Acoustical Society of America. 2015 Jan;137(1):30–41. pmid:25618036
  24. 24. Versluis M, Schmitz B, von der Heydt A, Lohse D. How snapping shrimp snap: through cavitating bubbles. Science. 2000 Sep 22;289(5487):2114–7. pmid:11000111
  25. 25. Au WW, Banks K. The acoustics of the snapping shrimp Synalpheus parneomeris in Kaneohe Bay. J Acoust Soc Am. 1998;103: 41–47.
  26. 26. Bohnenstiehl DR, Lillis A, Eggleston DB. The curious acoustic behavior of estuarine snapping shrimp: Temporal patterns of snapping shrimp sound in sub-tidal oyster reef habitat. PLoS ONE 11(1): e0143691. pmid:26761645
  27. 27. Lillis A, Eggleston DB, Bohnenstiehl DR. Estuarine soundscapes: distinct acoustic characteristics of oyster reefs compared to soft-bottom habitats. Marine Ecology Progress Series. 2014 May 28;505:1–7.
  28. 28. Staaterman E, Rice AN, Mann DA, Paris CB. Soundscapes from a Tropical Eastern Pacific reef and a Caribbean Sea reef. Coral Reefs. 2013 Jun 1;32(2):553–7.
  29. 29. Staaterman E, Paris CB, DeFerrari HA, Mann DA, Rice AN, D’Alessandro EK. Celestial patterns in marine soundscapes. Marine Ecology Progress Series. 2014 Aug 4;508:17–32.
  30. 30. Radford C, Jeffs A, Tindle C, Montgomery JC. Resonating sea urchin skeletons create coastal choruses. Mar Ecol Prog Ser. 2008;362: 37–43.
  31. 31. McCauley RD, Day RD, Swadling KM, Fitzgibbon QP, Watson RA, Semens JM. Widely used marine seismic survey air gun operations negatively impact zooplankton. Nature Ecology and Evolution. 2017;1(7); s41559–017.
  32. 32. Luczkovich JJ, Mann DA, Rountree RA. Passive acoustics as a tool in fisheries science. Transactions of the American Fisheries Society. 2008;137: 533–541.
  33. 33. Giorli G, Au WW, Neuheimer A. Differences in foraging activity of deep sea diving odontocetes in the Ligurian Sea as determined by passive acoustic recorders. Deep Sea Research Part I: Oceanographic Research Papers. 2016;107: 1–8.
  34. 34. Chapman NR, Price A. Low frequency deep ocean ambient noise trend in the Northeast Pacific Ocean. J Acoust Soc Am. 2011;129(5): 161–165.
  35. 35. Dahl PH, Miller JH, Cato DH, Andrew RK. Underwater ambient noise. Acoustics Today. 2007;3(1): 23–33.
  36. 36. Dunlop RA, Cato DH, Noad MJ. Your attention please: increasing ambient noise levels elicits a change in communication behaviour in humpback whales (Megaptera novaeangliae). Proceedings of the Royal Society of London B: Biological Sciences. 2010; prspb20092319.
  37. 37. Giorli G, Au WW, Ou H, Jarvis S, Morrissey R, Moretti D. Acoustic detection of biosonar activity of deep diving odontocetes at Josephine Seamount High Seas Marine Protected Area. J Acoust Soc Am. 2015;137(5): 2495–2501. pmid:25994682
  38. 38. Boyd JW, Varley J. The uses of passive measurement of acoustic emissions from chemical engineering processes. Chem Eng Sci. 2001;56(5); 1749–1767.
  39. 39. Preskitt LV, Vroom PS, Smith CM. A rapid ecological assessment (REA) quantitative survey method for benthic algae using photoquadrats with scuba. Pacific Science. 2004;58: 201–209.
  40. 40. Yentch CS, Yentsch CM, Cullen JJ, Lapointe B, Phinney DA, Yentsch SW. Sunlight and water transparency: cornerstones in coral research. J Exp Mar Biol Ecol. 2002;268(2): 171–183.
  41. 41. Sensors for sondes and monitoring. 2017;11:20. Available from: https://wwwwaterprobescom/sensors-for-sondes-and-monitoring.

Source: plos.org

Honey bee Royalactin unlocks conserved pluripotency pathway in mammals


Royal jelly is the queen-maker for the honey bee Apis mellifera, and has cross-species effects on longevity, fertility, and regeneration in mammals. Despite this knowledge, how royal jelly or its components exert their myriad effects has remained poorly understood. Using mouse embryonic stem cells as a platform, here we report that through its major protein component Royalactin, royal jelly can maintain pluripotency by activating a ground-state pluripotency-like gene network. We further identify Regina, a mammalian structural analog of Royalactin that also induces a naive-like state in mouse embryonic stem cells. This reveals an important innate program for stem cell self-renewal with broad implications in understanding the molecular regulation of stem cell fate across species.


The regulation of self-renewal and differentiation potential in mouse embryonic stem cells (mESCs) occurs through complex transcriptional networks orchestrated by conserved transcription factors1,2. Although differences exist in the specific signaling pathways that control self-renewal and lineage development, culture conditions that allow for derivation and maintenance of stem cells have been identified3,4,5,6,7,8. In particular, use of two small molecule inhibitors targeting MAPK/ERK Kinase (Mek) and glycogen synthase kinase-3 (GSK3) in addition to Leukemia inhibitory factor (LIF) in serum-free media permitted derivation of germline-competent ESCs that resemble the mature mouse inner cell mass (ICM)9. However, recent findings suggest that prolonged Mek1/2 suppression may have detrimental effects on the epigenetic and genetic integrity of mESCs, effectively limiting their developmental potential10,11. As such, additional methods of maintaining mESCs in an ICM state are required.

Though best-known as an epigenetic driver of queen development in A. mellifera12, the functional component of royal jelly, Major Royal Jelly Protein 1 (MRJP1, also known as Royalactin), has been shown to modulate biological function in a broad range of species12,13,14,15. Indeed, conservation of increased growth stimulation and cellular proliferation phenotypes in response to MRJP1 has been observed in murine hepatocytes16,17,18. While this indicates a functionally important role for this royal jelly protein in regulating cell state and fate, the full scope of its effects has not yet been well characterized19.

In this study, we identify Royalactin as a potent activator of a pluripotency gene network through modulation of chromatin accessibility, that maintains mESC self-renewal in the absence of LIF. Royalactin cultured cells also occupy a more naive ground state capable of generating chimeric animals with germline transmission. Finally, we identify a mammalian structural analog of Royalactin possessing similar functional capacity, uncovering a molecular conservation that supports distinct evolutionary processes.


Royalactin maintains mESC self-renewal and pluripotency

As mESCs provide a powerful model with which to study cellular regulation through pluripotency and differentiation programs, we first employed this model to dissect Royalactin’s mechanisms of action on mammalian cells. Upon LIF withdrawal (serum/–LIF), mESCs previously cultured in serum/+LIF media readily demonstrated a differentiated morphology as expected (Fig. 1a). Surprisingly, addition of Royalactin in the absence of LIF (serum/–LIF + Royalactin) for multiple passages resulted in dose-dependent formation of undifferentiated colonies that demonstrated similar morphology to those grown in the presence of LIF (Fig. 1a, Supplementary Figure 1a). Gene expression profiles of the cells grown in serum/–LIF + Royalactin further demonstrated a high degree of similarity relative to those cultured in the presence of LIF (Fig. 1b, Supplementary Figure 1b).

Fig. 1
Fig. 1

Royalactin maintains stemness in murine embryonic stem cells. a Representative images of J1 and R1 mESCs cultured in serum/+LIF, serum/−LIF, or serum/−LIF + Royalactin for 10 and 20 passages. After LIF withdrawal, mESCs rapidly differentiated, whereas cells cultured with Royalactin supported self-renewal with negligible differentiation. Scale bar, 200 μm. b Quantitative expression of pluripotency and differentiation-associated genes from a. Data are means ± SD (n = 2). c Mice bearing mESC-derived teratomas from J1 mESCs cultured three passages in +LIF and −LIF + Royalactin demonstrated retained pluripotency, and on high magnification (400×) produced differentiated ectodermal, mesodermal, and endodermal tissues. Scale bar, 80 μm. d RNA-seq log2-fold change values in transcript level of all genes in serum/+LIF or serum/–LIF + Royalactin J1 mESCs (passage 10) relative to serum/–LIF. e GO term analysis of differentially expressed genes from d. f ATAC-seq activity in J1 mESCs at passage 10. Each column is a sample, each row is an element. Samples and elements are organized by unsupervised k-means clustering. g GO term analysis of differentially accessible regions from f. h Representative images of Stat3, Esrrb, and Tfcp2l1 knockdown in J1 mESCs with serum/+LIF and serum/−LIF + Royalactin conditions and qPCR analysis of pluripotency and differentiation-associated genes from the same cells. Data are means ± SD (n = 2). Scale bar, 200 μm. RylA Royalactin

Having observed a robust stemness-maintenance effect of Royalactin in vitro, we next examined whether mESCs treated with Royalactin in serum/–LIF retain embryonic identity and developmental potential in vivo. Indeed, mESCs grown in serum/–LIF + Royalactin were grafted subcutaneously and gave rise within 6 weeks to large multi-differentiated teratomas (Fig. 1c). Collectively, these results indicate that Royalactin can functionally maintain self-renewal and pluripotency in mESCs.

Royalactin modulates chromatin and pluripotent networks

In the interest of understanding Royalactin’s effects on the transcriptome, RNA-seq analyses of serum/+LIF, serum/–LIF, and serum/–LIF + Royalactin cells were conducted. These analyses revealed a strong enrichment for canonical pluripotency genes and a suppression of lineage-specific genes in serum/–LIF + Royalactin cells at levels similar to those in mESCs cultured in serum/+ LIF (Fig. 1d). Gene Ontology (GO) term analysis of all genes differentially expressed in the presence of Royalactin revealed strong enrichment for genes involved with proliferation and stemness in the upregulated gene set, and an overrepresentation of developmental processes in the downregulated gene set (Fig. 1e). Similarly, analysis of chromatin accessibility using the assay for transposase-accessible chromatin using sequencing (ATAC-seq)20 of mESCs cultured in serum/–LIF + Royalactin and serum/+LIF conditions revealed similar patterns of increases in ATAC-seq signal relative to mESCs grown in serum/–LIF conditions (Fig. 1f), specifically at promoters (TSS; 14234 total peaks with 7373 gaining accessibility and 6861 losing accessibility; Supplementary Figure 1c), traditional enhancers (TE; 5356 total peaks with 2571 gaining accessibility and 2785 losing accessibility; Supplementary Figure 1d), and super enhancer regions (SEs; 127 of 231 elements gaining accessibility; Supplementary Figure 1e). As expected, high correlation was observed between ATAC-seq changes and RNA-seq experiments (Supplementary Figure 1c–e), with functional annotation revealing that the ATAC-seq changes were located near genes associated with pluripotency, metabolism, and differentiation (Fig. 1g). Furthermore, motif enrichment analysis revealed that TFs such as KLF5, KLF4, and SOX2 bound at high frequency to the Royalactin-upregulated SE constituents (Supplementary Figure 1f). Collectively, this suggested that regulatory regions are sensitive to Royalactin culture conditions and cause subsequent changes in gene expression.

In order to gain a molecular understanding of patterns of gene expression and identify candidate regulators of pluripotency in response to Royalactin, a transcriptional network analysis was performed that identified Stat3, Tfcp2l1, Esrrb, and Nanog as the most significant nodes (Supplementary Figure 1g). Subsequent experimentation revealed a dose-dependent effect for Royalactin in stimulating phospho-Stat3 activation concomitant with other pluripotency factors (Supplementary Figure 1h), which was sustained after 10 and 20 passages (Supplementary Figure 1i). In addition, knockdown of these transcription factors greatly diminished the mESC response to Royalactin, with the most significant effects being observed following Stat3 knockdown (Fig. 1h). As these findings suggested that Royalactin triggers activation of a Stat3-driven LIF-independent pathway on mESC self-renewal, further analysis of gene expression profiles from serum/+LIF and serum/–LIF + Royalactin mESCs were compared to identify 519 genes that are specifically activated in response to Royalactin (Supplementary Figure 1j). GO term analysis showed enrichment of metabolic and biosynthetic processes (Supplementary Figure 1k), reminiscent of mESCs cultured without serum in the presence of inhibitors targeting mitogen-activated protein kinase kinase and GSK3 (2i)21. Global expression profiles from serum/–LIF + Royalactin and 2i-cultured cells also clustered together by principal component analysis away from serum/+LIF-cultured cells (Supplementary Figure 1l). GO term enrichment analysis found that genes involved in basic metabolism, transcription, and development were responsible for this separation (Supplementary Figure 1m). Collectively, this suggested that Royalactin may be driving ground-state pluripotency in mESCs.

Royalactin treated mESCs mimic ground-state pluripotency

We next sought to test the hypothesis that Royalactin was driving a ground-state-like pluripotency state in mESCs. As expected, mESCs cultured in 2i + LIF media maintained pluripotency and sustained expression of a Rex1 GFP pluripotency marker22, while those in 2i base media without inhibitors (0i) readily differentiated (Fig. 2a, b, Supplementary Figure 2). Remarkably, addition of Royalactin in the absence of inhibitors (0i + Royalactin) for multiple passages maintained undifferentiated GFP positive colonies with similar gene expression profiles to 2i + LIF cultured cells (Fig. 2a, b, Supplementary Figure 2). In addition, injection of 0i + Royalactin cultured cells into mouse blastocysts generated chimeric animals with germline transmission, highlighting the robust effects of this protein in vivo (Fig. 2c, Supplementary Table 1).

Fig. 2
Fig. 2

Royalactin drives a ground-state-like pluripotency state in mESCs. a Representative images of J1 and R1 mESCs cultured in serum-free media in the presence (2i + LIF) or absence (0i) of MAPKKi, GSK3i, and LIF for 10 and 20 passages. mESCs rapidly differentiated in 0i, whereas cells cultured with Royalactin (0i + Royalactin) supported self-renewal with negligible differentiation. Scale bar, 200 μm. b Quantitative expression of pluripotency and differentiation-associated genes from a. Data are means ± SD (n = 2). c Chimeras with germline transmission formed by CGR 8.8 mESCs treated for ten passages with 0i + Royalactin. d RNA-seq log2 fold change values in transcript level of all genes in 2i or 0i + Royalactin (0i + RylA) J1 mESCs (passage 10) relative to 0i. e GO term analysis of the differentially expressed genes from d. f J1 mESCs cultured in serum/+LIF, serum/−LIF + Royalactin, 2i + LIF, and 0i + Royalactin for ten passages are projected onto the first two principal components. All genes with mean normalized read counts larger than 10 were considered for principal component analysis (PCA). g Distribution of genes contributing to principal component 1 (PC1) in f, and GO enrichment analysis of genes most strongly contributing to PC1 separation. RylA Royalactin

RNA-seq and GO-term analyses further demonstrated marked similarities in enriched genes between 2i + LIF and 0i + Royalactin cells (Fig. 2d, e), and global expression profiles from 2i + LIF, 0i + Royalactin, serum/+LIF, serum/–LIF + Royalactin, and serum/–LIF cells demonstrated a clear clustering by principal component analysis of 0i + Royalactin cells nearer those cultured in 2i + LIF than those cultured in serum/+LIF (Fig. 2f). GO term enrichment analysis found that genes involved in basic metabolism, transcription, and development were responsible for this separation (Fig. 2g). These data suggest that Royalactin treatment is accompanied by a profound metabolic reprogramming resembling the 2i + LIF state, mimicking the environment of the mature mouse ICM.

Identification of Royalactin mammalian analog

We next wondered whether a homolog of Royalactin existed in mammals. Sensitive searches of sequence databases using iterative PSI-BLAST23, as well as aiming HHPRED sequence and structural profiles against the human and mouse proteomes24 did not reveal any Royalactin orthologs. However, the latter computational tool revealed that Royalactin is distantly related to an existing structure in the PDB database25, a secreted salivary gland protein (SGP) from the sand fly, L. longipalpis (PDB ID: 3Q6K)26. We then used this structure––a six-bladed β-propeller fold with no additional domains—as an accurate template for MODELLER27, yielding a high confidence model for the Royalactin fold (Fig. 3a). The resulting superposition of Royalactin and SGP sequences was then used to seed new and more precise HHPRED scans of the human proteome, in search of a possible structural and functional analog of the Royalactin/SGP β-propeller fold. Fitting the description of a secreted, single domain chain, with a predicted 6-bladed β-propeller architecture, only one protein, the provisionally named NHL Repeat Containing 3 (NHLRC3), arose as a potential candidate, with striking fold similarity to the Royalactin model (Fig. 3a). Although no known function of NHLRC3 has been identified to date, single-cell RNA-seq analyses of early mouse embryos revealed that it is expressed starting in E4.5 embryos, and that its expression increases steadily thereafter (Supplementary Figure 3a). To elucidate whether it served a functional purpose in stemness maintenance in mESCs similar to that observed with Royalactin, recombinant mouse NHLRC3 was added to mESC culture in the presence of serum/–LIF (serum/–LIF + NHLRC3) as well as 0i (0i + NHLRC3). As seen with Royalactin, NHLRC3 maintained mESCs in an undifferentiated state in both culture conditions for multiple passages (Fig. 3b, c, Supplementary Figure 3b, c), with expected changes in gene expression (Fig. 3d, e). Additionally, injection of 0i + NHLRC3 cultured cells into mouse blastocysts generated chimeric animals with germline transmission, highlighting the robust effects of this protein in vivo (Supplementary Figure 4, Supplementary Table 1). Thus, NHLRC3 appears to be a mammalian pluripotency maintenance factor, whose existence demonstrates a remarkable conservation of macromolecular structure and function. We renamed NHLRC3 as Regina due to its conservation of functions with those of Royalactin and the queenmaker Royal Jelly.

Fig. 3
Fig. 3

The mammalian structural analog of Royalactin maintains naive and ground-state pluripotency in mouse embryonic stem cells. a Computational modeling predicts the structure of Royalactin (left), allowing for identification of a highly structurally analogous protein, NHLRC3 (center). Superimposition of these models (right) demonstrates striking similarity between them. b Representative images of J1 and R1 mESCs cultured in serum/+LIF, serum/–LIF, or serum/−LIF + NHLRC3 for 10 and 20 passages. After LIF withdrawal, mESCs rapidly differentiated, whereas cells cultured with NHLRC3 supported self-renewal with negligible differentiation. Scale bar, 200 μm. c Representative images of J1 and R1 mESCs cultured in serum-free media in presence (2i + LIF) or absence (0i) of MAPKKi, GSK3i, and LIF for 10 and 20 passages. mESCs rapidly differentiated in 0i, whereas cells cultured with NHLRC3 (0i + NHLRC3) supported self-renewal with negligible differentiation. Scale bar, 200 μm. d Quantitative expression of pluripotency and differentiation-associated genes from b. Data are means ± SD (n = 2). e Quantitative expression of pluripotency and differentiation-associated genes from c. Data are means ± SD (n = 2)

In summary, our results demonstrate an unexpected capacity for Royalactin as a pluripotency factor that confers self-renewal and promotes emergence of the naive pluripotent gene regulatory network, and identify Regina as a factor that can promote ground-state pluripotency in mESCs. A better understanding of the interaction of Royalactin and Regina with genetic pathways and biochemical processes conserved in evolution, and how the different pluripotency networks function independently or synergistically to maintain stem cell self-renewal, will advance our efforts to better control stem cell fate, and provide a platform for dissection of the pluripotent state. Our findings and the discovery of Regina thus support the intriguing idea that profound molecular conservation underlies even the most evolutionarily novel traits. Future work in dissecting the mechanistic action of Royalactin in mammalian cells, including further characterization of its mammalian structural analog Regina, will shed new light on mammalian pluripotency and provide additional means to enhance optimal maintenance and derivation of ESCs for therapeutic application and regenerative medicine.


Embryonic stem cell culture

J1 and R1 mESCs (gift from Howard Y. Chang) were grown on 0.2% gelatin-coated (Sigma G6144) tissue culture plates. Cells were maintained for a minimum of ten passages in mESC serum medium containing KnockOut™ D-MEM (Life Technologies 10829), 15% HyClone™ fetal bovine serum (FBS; Thermo Scientific™ SH30396.03), 100 U/ml Penicillin-Streptomycin (P/S; Life Technologies 15140), 1% MEM non-essential amino acids (Life Technologies 11140), 1% GlutaMAX™ (Life Technologies 35050), and 0.1 mM 2-mercaptoethanol (Life Technologies 21985). Mouse leukemia inhibitory factor (LIF; Millipore ESG1107; 1000 U/mL), purified Royalactin (0.5 mg/mL), or purified NHLRC3 (0.125 mg/mL) were added to culture as indicated. Cells were passaged every 2 to 3 days using Trypsin-EDTA (0.25%; Life Technologies 25200) and seeded at 15,000 cells/cm2. Media and protein were changed daily.

For cultures under 2i conditions, J1, R1, and Rex1-GFP mESCs were grown on Poly-L-ornithine (Sigma-Aldrich)/Laminin (Fisher Scientific 23017–015) coated plates and CGR8.8 mESCs were grown on Matrigel (Corning 354277) plates coated according to manufacturer’s specifications. All cells were maintained a minimum of ten passages in serum-free media containing: 1:1 Neurobasal:DMEM-F12 base (Thermo Scientific 21103049), 1% Glutamax, 1% high insulin N2 (Thermo Scientific 17502001), 1% B27 supplement (Thermo Scientific 12587001), 0.1 mM 2-mercaptoethanol, 1% Penicillin-Streptomycin, 1% MEM non-essential amino acids, 1% sodium pyruvate (Thermo Scientific 11360070). Mouse leukemia inhibitory factor (LIF; Millipore ESG1107; 1000 U/mL), 1 μM PD0325901 (Selleckchem), 3 μM CHIR99021 (Selleckchem), purified Royalactin (0.5 mg/mL), or purified NHLRC3 (0.125 mg/mL) were added to cultures as indicated. Cells were passaged every 2 to 3 days using Accutase (Stemcell Technologies 07920) and seeded at 15,000 cells/cm2. Media and protein were changed daily.

Production of recombinant Royalactin and NHLRC3

FLAG-MRJP1-His (Genbank ID# NM_001011579.1) was cloned into LakePharma’s proprietary antibiotic selection vector and transfected by electroporation into suspension CHO parental cells. The FLAG-MRJP1-His stable cell line was generated after 2 weeks of antibiotic selection period. Purification of FLAG-MRJP1-His was achieved by a two-step chromatography method. Conditioned media was centrifuged, filtered, and loaded onto an anion exchange chromatography (AEX) resin pre-equilibrated with 20 mM Tris pH 7.5. FLAG-MRJP1-His was eluted by increasing sodium chloride concentration and fractions containing the protein were pooled together. This sample was further polished by a second immobilized metal (Ni) affinity chromatography (IMAC) step using increasing concentrations of imidazole for elution. SDS-PAGE was performed for each fraction and the ones containing FLAG-MRJP1-His were pooled together for dialysis. The final formulation for FLAG-MRJP1-His was 200 mM NaCl in 30 mM HEPES pH 7.0.

For production of supernatants containing recombinant NHLRC3, suspension CHO cells were seeded at 350,000 cells/mL into 90% CD OptiCHO Medium (Thermo Scientific 12681011) containing 6mM L-glutamine (Thermo Scientific 25030–081) and 10% CHO CD EfficientFeed (Thermo Scientific A1023401). CHO cells were grown at 37 °C for 5 days, shaking constantly. The cell suspension was centrifuged at 10,000 × g for 40 min and the supernatant run through a 0.22 μm filter. Presence of concentrated NHLRC3 was verified by western blot. The filtered supernatant was concentrated 35-fold and used directly in cell culture assays.

RNA extraction and quantitative PCR

Total RNA was isolated using TRIzol® (Life Technologies) and RNeasy Kit (QIAGEN) according to the manufacturer’s protocol. cDNA was made with Superscript VILO (Life Technologies). All primers (Supplementary Table 2) were tested for efficiency and single products confirmed. qPCR analyses were performed on the Light Cycler 480II (Roche).

Lentiviral expression and viral production

pLKO vectors were a gift of Alejandro Sweet-Cordero. N103 vector was a gift of Howard Y. Chang. Sequence verified constructs were used to produce lentivirus using pRSV (Addgene plasmid #12253), pMD2.G (Addgene plasmid #12259), and pMDLg/pRRE (Addgene plasmid #12251). 293Ts were maintained in G418 (Sigma-Aldrich G8168). Plasmids were transfected using Lipofectamine 2000 following manufacturer’s protocol (Thermo-Fisher Scientific 11668). After 12 h, media was changed to viral production media (DMEM, 10% FBS, 1% P/S, 20 mM HEPES). After 48 h, media was collected, spun to remove cell debris, and lentiviral-containing supernatant was added to Lenti-X™ Concentrator (CloneTech). Following incubation at 4 ˚C for >4 h, the mixture was spun at 500 × g for 45 min, the pellet resuspended in mESC media, and frozen at −80 ˚C.

Lentiviral transduction of mESCs

mESCs were plated at a density of 30,000/cm2. After 12 h, virus was added with 6 µg/mL polybrene. Media was changed 12 h later and puromycin selection began 48 h post-transduction.

Cell culture for teratoma formation assay

J1 mESCs were cultured in serum-free media (as described above) with addition of 1000 U/mL mouse LIF, 1 µM PD0325901, and 3 µM CHIR99021, or 0.5 mg/mL Royalactin for three passages. Cells were grown in suspension on Corning Costar Ultra-Low attachment plates (Sigma-Aldrich). Wells were seeded in duplicate at a cell density of 100,000/well, and media and protein were changed daily. To split, colonies were first pelleted by centrifugation at 1500 × g, suspended in TrypLE (Life Technologies), incubated at 37 °C for 5 min, diluted with PBS (Life Technologies) and pelleted. Cells were counted and re-seeded at a density of 100,000/mL.

Teratoma generation and histopathology

All animal studies were conducted in accordance with Stanford University animal use guidelines and were approved by the Administrative Panel on Laboratory Animal Care (APLAC). J1 mESCs were mixed with Matrigel (BD 356237) prior to being subcutaneously injected into 8-week-old female SCID/Beige mice (Charles River) on each flank. Four weeks after injection, the mice were euthanized and the teratomas were harvested. All animal studies were approved by Stanford University IACUC guidelines. For histological analysis, slides were stained with hematoxylin and eosin (H&E) following manufacturer’s instructions. Analyses were performed by a board-certified veterinary pathologist.

Chimera experiments

CGR 8.8 mESCs were grown in serum-free media (as described above) with addition of 1000 U/mL mouse LIF, 1 µM PD0325901, and 3 µM CHIR99021 (2i + LIF), 0i + Royalactin, or 0i + NHLRC3 (as noted) for ten passages. Media was changed daily. Cells were passaged using Accutase (Stemcell Technologies 07920) and suspended in M2 media for injection.

Protein extraction and western blot analysis

Cellular extracts were prepared using lysis buffer containing 50 mM Tris HCL (pH 7.5), 250 mM NaCl, 1% NP-40, 0.5% Na-deoxycholate, 0.1% SDS, 1 mM phenylmethylsulfonyl fluoride, Halt™ Protease, and Phosphatase Inhibitor Cocktail (ThermoFisher Scientific). Extracts were run on a 4–12% Bis-Tris gel (Novex) and transferred onto PVDF membranes. Blots were blocked in 5% milk PBS-T (TBS for phospho-specific) for 1 h at room temperature followed by overnight incubation at 4 ˚C with primary antibodies. HRP-conjugated secondary antibodies were used at 1:10,000. Antibodies used in this study include Nanog (ReproCELL, RCAB001P, 1:1000), Klf2 (Millipore, 09–280, 1:1000), Esrrb (Perseus Proteomics, PP-H6705–00, 1:500), Stat3 (Cell Signaling, 124H6, 1:1000), pStat3 Y705 (Cell Signaling, D3A7, 1:1000), Sox2 (Santa Cruz, sc-17320, 1:250), Tfcp2l1 (R&D, AF5726, 1:250), NHLRC3 (OriGene, TA336106, 1:500), anti-HA (Cell Signaling, C29F4, 1:1000), and Actin-HRP (Santa Cruz, sc-1616, 1:2500). Uncropped scans of the most important blots are included in Supplementary Figure 5.

RNA-seq library construction

RNA was extracted using TRIzol and purified on column with the RNeasy Mini Kit (Qiagen). Ribosomal RNA was depleted with the Ribo-Zero Gold rRNA Removal Kit (Illumina). RNA was lyophilized, suspended in 10 μL of water and fragmented to an average size of 200 base pairs using the Ambion RNA Fragmentation Kit (AM8740), and purified using Zymo clean and concentrator 5 columns. 3′ Phosphorylation, adapter ligation, reverse transcription, immunoprecipitation, circularization, amplification, and PAGE separation were performed using the FAST-iCLIP library construction method as previously described28. The quality of the libraries, including size distribution and molarity, was assessed on a BioAnalyzer High Sensitivity DNA chip (Agilent). The libraries were then multiplexed and sent for sequencing on an Illumina NextSeq 400 High Output machine for 1 × 75 cycles. Sequencing data deposited under GEO GSE81799.


ATAC-seq was performed on 50,000 J1 mESCs20. Cells were grown in serum mESC media (as described above) in serum/+LIF, serum/–LIF, or serum/–LIF + Royalactin for ten passages. Cells were washed with PBS (Life Technologies), trypsinized with Trypsin-EDTA (0.25%), quenched with serum mESC media, washed with PBS, before nuclear isolation with NP-40. Nuclei were resuspended in a transposase reaction mix containing 25µL 2× TD buffer, 2.5 µL Transposase (Illumina) and 22.5 µL of nuclease free water with sequencing adapters. Final libraries were purified on column using the QIAquick PCR Purification Kit (Qiagen) per the manufacturer’s protocol as well as with Agencourt AMPure XP beads (Beckman Coulter) to remove any remaining free adapters. The quality of the libraries, including size distribution and molarity, was assessed on a BioAnalyzer High Sensitivity DNA chip. Libraries were then multiplexed and sent for deep sequencing on the Illumina HiSeq 2500 machine for 2 × 50 cycles. Sequencing data deposited under GEO GSE81799.

RNA-seq data analysis

Reads were aligned to the mouse reference genome (build mm9) using Tophat. A maximum of a default 2 mismatches was allowed for read alignment. Gene counts were calculated using the HTSeq-count utility29 and used as an input for differential gene expression analysis with DESeq version 1.20.030. Genes with a p-value of 0.05, as well as a fold change of 2 were selected for further analysis. Validation of top differentially regulated genes was performed with quantitative reverse transcription polymerase chain reaction. Further network analysis on differentially significant genes was performed using NetworkAnalyst31. For RNA-seq analysis, GO terms were obtained using DAVID and its default parameters.

PCA analysis was performed using samples as indicated. The genes that led to the maximum amount of variance (PC1) were selected and GO terms obtained using the GO Consortium. Samples from different libraries were normalized using shifted log of normalized counts using DESeq. The ‘plotPCA’ function, which is a part of DESeq2, was used to construct the PCA plots.

ATAC-seq data analysis

Reads were aligned to the mouse reference genome (build mm9) using Bowtie. The ATAC-seq regions were divided into separate analyses: correlation with closest TSS, correlation with 5356 traditional enhancer regions present in the mm9 genome, and correlation with 361 super-enhancer regions discovered for the mm9 genome28. The ATAC-seq signals for serum/+LIF, serum/–LIF, and serum/–LIF + Royalactin after ten passages were compared using DESeq, and the results are represented in the heatmaps. The heatmaps for TSS regions, traditional enhancers, super-enhancers, and differential peaks were produced using unsupervised clustering methods, which used the normalized signal values obtained by quantile normalization, to extract transitions between two states: upregulated and downregulated. The differential peaks between serum/–LIF + Royalactin and serum/–LIF were used for correlation with RNA-seq. The peaks were filtered on the basis of a p-value threshold of 0.05 as well as fold change. Boxplots were produced using the ‘BOXPLOT’ function in R. p-value was calculated using the student’s t-test.

GO terms for peaks differentially expressed in serum/–LIF and serum/–LIF + Royalactin was performed using GREAT. The significant GO terms were filtered to only include GO terms associated with pluripotency and GO terms associated with metabolism. For pluripotency related GO terms, biological processes including morphogenesis, development, proliferation, and stem cell processes were analyzed. For metabolic GO terms, biological processes that were related to metabolism and biosynthetic processes were chosen. Motif analysis for the differential peak lists was performed using HOMER with all differential peaks used as background.

Structural modeling and Royalactin analog identification

As implemented at the MPI Toolkit (http://toolkit.tuebingen.mpg.de), HHPRED enables sensitive searches of sequence and structural databases through the assembly of profile Hidden Markov Models (HMMs) from a seed sequence, with multiple iterations of Hhblits (a more sensitive and faster program than PSI-BLAST) and PSIPRED (a very accurate secondary structure prediction program)24. The detection of a six-bladed β-propeller fold for Royalactin from the top salivary gland protein (PDB ID: 3Q6K) HHPRED hit was accompanied by a significant score of 177.4, an E-value of 6e−28, and a 28% amino acid identity from the structure-guided overlap of the mature, 413 residue honeybee Royalactin, with the 381 amino acid sand fly SGP. This structural alignment was used by MODELLER27 to guide a secure template-guided three-dimensional model of Royalactin (with a VERIFY3D score of 119.7)32, and also to nucleate a more sensitive search by HHPRED for a structurally analogous protein (to the greater Royalactin/SGP family) in the human and mouse proteomes. This latter screen, filtered by the signal peptide, single domain, and six-bladed β-propeller fold constraints common to Royalactin and SGPs, yielded NHLRC3 (Uniprot IDs: Q5JS37 and Q8CCH2, for the human and mouse orthologs, respectively) as the sole analog candidate. A three-dimensional model of the NHLRC3 β-propeller was then built by MODELLER on the best six-bladed β-propeller template recognized by HHPRED, Peptidyl-alpha-hydroxyglycine alpha-Amidating Lyase (PDB ID: 3FVZ; at a significant score of 125.6, E value of 1.7e-18, and amino acid identity of 24%). The comparison and visualization of Royalactin and NHLRC3 structural models were in turn performed by PyMOL (www.pymol.org). We recognize that β-propeller folds in general (irrespective of ‘blade’ number) are consistently used as interaction scaffolds and preferred binding platforms in the cell33. The structural models are available upon request.


Listening for the Rhythm of a Conscious Brain

The question of whether someone is conscious of themselves and their environment may initially appear to be a philosophical thought experiment. However, for many families and clinicians throughout the world, it is a challenge with profound implications for decision-making after severe brain injury. The current standard approach to answering the question of consciousness in the clinic is to observe the patient’s behaviour for evidence of purposeful action, such as tracking a moving object. However, because of inequalities in healthcare provision both between and within countries, many patients never receive standardized behavioural assessment. Consequently, misdiagnoses of so-called prolonged disorders of consciousness (PDOC) occur at alarmingly high rates: ~40% when standardized assessment is not available and ~30% when a standardized assessment is not conducted longitudinally (Wannez et al., 2017). With the recent confirmation from the UK’s Supreme Court that withdrawal of clinically assisted nutrition and hydration from individuals with PDOC may occur without the involvement of the courts (UK Supreme Court, 2018), access to accurate diagnostic tools is all the more imperative. In this issue of Brain, Engemann and co-workers present results from a trans-European collaboration that offer hope for accurate, accessible, and objective diagnoses of consciousness in this challenging patient group (Engemann et al., 2018).

Over the past decade, researchers have identified an array of markers within the EEG of patients with PDOC that differ between those who are conscious (i.e. the minimally conscious state) and those who are entirely unconscious [i.e. the vegetative state, also known as unresponsive wakefulness syndrome (UWS)]. These markers may be task-based changes in the EEG that occur in response to stimuli, such as sounds or verbal instructions, or task-free features of the patient’s EEG at rest. Engemann et al. (2018) apply advances in machine learning to the largest ever dataset of PDOC brain data and demonstrate that a combination of theoretically- and empirically-motivated EEG markers can accurately diagnose patients’ levels of consciousness.

To test the efficacy of a machine learning method, researchers first require a set of training data for which they and their algorithm know the ‘truth’—i.e. which data correspond to patients who are conscious by standardized assessment, and which belong to those who are not conscious. The researchers then apply the trained model to previously unseen test data from one patient in order to estimate their ‘true’ diagnosis. Crucially, the model described by Engemann et al. (2018) accurately diagnoses patients even when the training and test data were recorded in different countries, with different EEG equipment, and different EEG protocols. The success of this model, therefore, creates the real possibility for an objective online tool that families and clinicians across the world could use with locally recorded EEG data, and that could consequently improve diagnostic accuracy and subsequent decision-making. Indeed, through a range of stress tests, the authors demonstrate that their model performs well with only a few minutes of data recorded from 16 electrodes; fewer than the 19-channel EEG montage typically available in clinical EEG protocols.

While the diagnostic accuracy of the model is impressive, there is nevertheless potential for the model to misdiagnose patients due to fluctuations in levels of consciousness over time. Indeed, clinicians must conduct multiple behavioural assessments of consciousness before they can achieve a stable and accurate diagnosis (Wannez et al., 2017). Equally, the level of consciousness that is evident in a patient’s EEG is likely to fluctuate both within and across days, and may therefore require multiple EEG sessions before a stable diagnosis can be reached. Importantly, as the diagnostic model described by Engemann et al., 2018 is robust with brief EEG recordings from a range of EEG systems and protocols, these necessarily longitudinal assessments of EEG may be more tractable now than ever before.

The authors report that power in the alpha band of the EEG (8–12 Hz) was consistently the most informative feature used by the models for accurate diagnosis. Alpha oscillations are the most prominent rhythm in the healthy brain and have long been associated with neuronal idling, such that high alpha power was thought to reflect a state of underlying cortical inactivity. While the alpha rhythm can be seen with the naked eye in the EEG of healthy individuals, it is considerably reduced in PDOC (Figure 1). Resting state alpha oscillations in the healthy brain are thought to be generated within the thalamus and its connections with cortex (Roux et al., 2013). Thalamic damage is perhaps the most consistent structural impairment observed across PDOC, and differentiates minimally conscious state from unresponsive wakefulness syndrome (Fernandez-Espejo et al., 2010). Therefore, EEG alpha power may be the most informative marker in this diagnostic model precisely because it reflects the relative level of damage to brain structures that are known to underlie a patient’s level of consciousness.

Figure 1.

The role of alpha oscillations in perception. The thalamus (highlighted in pink) is highly connected to the cortex and is implicated in generating alpha oscillations through thalamo-cortical loops. A schematic alpha oscillation (top right) demonstrates the proposed active inhibition mechanism of boosting (green rectangle) or inhibiting (red rectangle) sensory processing by means of power changes, and the perceptual boost observed at specific phases of the alpha oscillation. The power spectrum (bottom) of a healthy individual (black line) shows a clear peak in the alpha band (at 10 Hz), which is entirely absent from a typical patient with a diagnosis of unresponsive wakefulness syndrome [grey line; data taken from Beukema et al. (2016); doi:10.1016/j.nicl.2016.08.003].

A complementary interpretation is that EEG alpha power also reflects an important aspect of the capacity for contents of consciousness in PDOC. Indeed, recent evidence suggests that alpha oscillations play a functional role in both cognition and perception. For example, visual perception relies on alpha oscillations to segment incoming visual information into bite-sized snapshots of ~100 ms for visual cortex to process (VanRullen, 2016). When we pay attention to one side of our visual field, alpha power decreases over contralateral visual cortex and increases over ipsilateral visual cortex. The active inhibition hypothesis of alpha oscillations (Jensen and Mazaheri, 2010) proposes that increases in alpha power reflect inhibition of processing in task-irrelevant brain regions, while decreases in alpha power provide a boost to neural processing in task-relevant regions. Furthermore, in support of a functional role of alpha oscillations, the extent to which alpha becomes lateralized during spatial attention correlates with detection accuracy of visual targets (see Jensen and Mazaheri, 2010 for a review). Therefore, without a functional alpha rhythm to orchestrate the activity of relevant cortical regions, PDOC cortex becomes incapable of functional perception and cognition. Patients would not perceive the world in discrete perceptual cycles and would be unable to selectively attend to sensory stimuli, such as a relative’s voice or the instructions of a rehabilitation specialist. While the alpha power markers used in the diagnostic model described by Engemann et al. (2018) were summarized across the whole head, recent evidence of multiple, functionally dissociable neural sources of alpha oscillations (Sokoliuk et al., 2018) suggests that a more fine-grained picture of the relative preservation of both levels and contents of consciousness in PDOC may be possible.

The importance of resting alpha power in this diagnostic model also raises the question of whether it is futile to pursue task-based, or ‘active’, methods for detecting consciousness. Perhaps the most well-known task-based paradigm involves asking patients to imagine that they are playing tennis and looking for activity in task-appropriate regions of their brain in order to infer that they obeyed the instruction, and are therefore conscious (Owen et al., 2006). Evidence of consciousness from such ‘active’ methods is certainly compelling and appeals to our willingness to consider that someone is conscious only if they can show us with their (physical or mental) behaviour. However, a recent large-scale study of PET and functional MRI data showed that active paradigms were less useful for diagnosis than simple task-free measures (Stender et al., 2014). Furthermore, in the study described in this issue of Brain, task-based markers of consciousness contributed little to the accuracy of diagnoses compared with task-free, passive, markers. Nevertheless, in our opinion, both task-free and task-based data are vital for solving the clinical challenges posed by PDOC. While task-free data may provide evidence for the preservation of a set of brain functions and structures that are minimally required for demonstrating consciousness through overt or covert behaviour, they cannot answer a host of important questions about the patient’s cognitive abilities. For example, can the patient understand what is being said to them by their families? Can the patient maintain attention for sufficient time for rehabilitation efforts to succeed? Can the patient form or retrieve memories of important events? The answers to these questions may arguably assist families more in their decision-making than abstract questions of whether someone is ‘conscious’. It may be that, in future, the method described in the current study will allow accurate diagnosis of the global state of consciousness of a given patient, while subsequent task-based examinations can outline the cognitive profile of the patient to help guide decision-making and rehabilitation efforts.

Finally, the study by Engemann et al. (2018) is a landmark in open-source big data in the field of PDOC. Indeed, the authors performed all analyses in the study with the open-source programming language of Python, and made the machine learning ‘recipe’ publicly available online alongside the scripts for EEG feature extraction. For non-behavioural measures of consciousness to achieve a true impact on clinical practice, the field must continue to generate and share larger and larger open access datasets, thus developing more accurate methods to assist families and clinicians with their most difficult decisions.

The ‘Graying’ of T Cells

Scientists pinpoint metabolic pathway behind age-related immunity loss

immune cell attack

Researchers have identified a defective metabolic pathway in aging immune cells.

The elderly suffer more serious complications from infections and benefit less from vaccination than the general population. Scientists have long known that a weakened immune system is to blame but the exact mechanisms behind this lagging immunity have remained largely unknown.

Now research led by investigators at Harvard Medical School suggests that weakened metabolism of immune T cells may be partly to blame.

The findings, published Dec. 10 in PNAS and based on experiments in mouse immune cells, pinpoint a specific metabolic pathway called one-carbon metabolism that is deficient in the aged T cells of rodents. The work also suggests possible ways to restore weakened immune function with the use of small-molecule compounds that boost T-cell performance.

“We believe our findings may help explain the basic malfunction that drives loss of immune defenses with age,” said senior study author Marcia Haigis, professor of cell biology in the Blavatnik Institute at Harvard Medical School. “If affirmed in further studies, we hope that our findings can set the stage for the development of therapies to improve immune function.”

The role of T cells in the immune system is twofold: attacking illness-causing cells like bacteria, viruses and cancer and “remembering” past invaders—the body’s way of ensuring that it can spot a threat and mount a rapid defense during subsequent encounters with the same pathogens. In a healthy person, T cells circulate in the blood and quietly scan the body for threats using proteins on the cell’s surface. If a T cell encounters another cell it deems dangerous, the T cell undergoes activation, a molecular cascade in which it switches from surveillance mode to attack mode. The activated cells then rapidly replicate to build an army and destroy the enemy.

First, the researchers looked for overall differences between old and young T cells. They isolated T cells from the spleens of young and old mice and noticed that, in general, older mice had fewer T cells.  Next, to gauge the cells’ immune fitness, the researchers activated the T cells by mimicking signals normally turned on by pathogens during infection. The older T cells showed diminished activation and overall function in response to these alarm signals. Specifically, they grew more slowly, secreted fewer immune-signaling molecules and died at a much faster rate than young T cells. The researchers also observed that aged T cells had lower metabolism, consumed less oxygen and broke down sugars less efficiently. They also had smaller than normal mitochondria, the cells’ power-generators that keep them alive.

It was as if these older immune cells had lost their “appetite” and their ability to process fuel into energy, Haigis and her colleagues observed.

To pinpoint the metabolic pathways behind this malfunction, the scientists analyzed all the different proteins in the cells, including those that might be important for coaxing a T cell from dormancy into a fighting state. The team found that the levels of some 150 proteins were lower-than-normal upon activation of the aged T cells, compared with young T cells. About 40 proteins showed higher than normal levels in aged versus young T cells. Many of these proteins have unknown functions, but the researchers found that proteins involved a specific type of metabolism, called one-carbon metabolism, were reduced by nearly 35 percent in aged T cells.

One-carbon metabolism comprises a set of chemical reactions that take place in the cell’s mitochondria and the cell cytosol to produce amino acids and nucleotides, the building blocks of proteins and DNA. This process is critical for cellular replication because it supplies the biologic material for building new cells.

The team’s previous work had shown that one-carbon metabolism plays a central role in supplying essential biological building blocks for the growing army of T cells during infection. So, the scientists wondered, could adding the products of this pathway to weakened T cells restore their fitness and function?

To test this hypothesis, the team added two molecules—formate and glycine, the main products of one-carbon metabolism—whose levels were markedly reduced in aged T cells. Indeed, adding the molecules boosted T cell proliferation and reduced cell death to normal levels.

The researchers caution that while encouraging, the effects were observed solely in mouse cells in lab dishes rather than in animals and must be confirmed in further experiments.

Chicken Bones May Be the Legacy of Our Time

A new study argues that the sheer abundance of chicken consumption, coupled with the strange skeletons of modern chickens, will leave a unique fingerprint


Some experts say we are now in the era of the “Anthropocene,” a term used to describe humans’ unprecedented influence on the planet. When our civilization is long gone, the Earth will continue to bear the effects of the time we spent here—effects like nuclear isotopes in sedimentary rock, and the fossilized remains of plastic on the ocean floor and concrete on land. But perhaps more than anything else, according to a new study, the great legacy of our time will be chicken bones. Lots and lots of chicken bones.


Writing in Royal Society Open Science, a team of researchers argues that the remains of domesticated chickens (Gallus gallus domecustis) will be a major and unique marker of our changing biosphere. For one thing, there are just so many of them. With a standing population of more than 22.7 billion, domesticated chickens far outnumber the world’s most abundant wild bird—the red-billed quelea, which has a population of about 1.5 billion. According to James Gorman of the New York Times, if you combined the mass of all these chickens, it would be greater than that of all other birds.

The world is home to such a huge number of chickens because humans can’t stop eating them. Chicken consumption is growing faster than the consumption of any other type of meat—more than 65 billion chickens were slaughtered in 2016 alone—and it is on pace to surpass pork soon as the world’s most consumed meat.

With an abundance of chicken dinners comes an abundance of chicken remains. In the wild, bird carcasses are prone to decay and are not often fossilized. But organic materials preserve well in landfills, which is where many chicken remains discarded by humans end up. Thus, these chicken bones don’t degrade, according to the study authors—they mummify. For this reason, lead study author Carys E. Bennett tells Sam Wong of New Scientist that chickens are “a potential future fossil of this age.”

The modern chicken’s strange and singular features also make it a good candidate to represent the current era of human-directed change. The domestication of chickens started around 8,000 years ago, but humans have come up with a number of innovations to feed our growing hunger for chicken products. Modern broiler chickens, which is the variety farmed for meat, are bred to be four or five times heavier than they were in the 1950s. They are transported to slaughterhouses once they reach an age of between five and seven weeks, which may seem like a short lifespan, but in reality, they would not be able to survive much longer.

“In one study, increasing their slaughter age from five weeks to nine weeks resulted in a sevenfold increase in mortality rate,” the study authors write. “The rapid growth of leg and breast muscle tissue leads to a relative decrease in the size of other organs such as the heart and lungs, which restricts their function and thus longevity. Changes in the centre of gravity of the body, reduced pelvic limb muscle mass and increased pectoral muscle mass cause poor locomotion and frequent lameness.”

These chickens are, unsurprisingly, unlike any the world has seen before. The study authors compared data on modern broilers to zooarchaeological information recorded by the Museum of London Archaeology. Today’s domestic chickens are descended from a bird called the red junglefowl, Gallus gallus, and related species that might have bread with G. gallus, Andrew Lawler and Jerry Adler explain for Smithsonian magazine. The researchers found that between the 14th and 17th centuries, domestication caused chickens to become noticeably larger than their wild progenitors. But those chickens had nothing on the fowls of today. “There has been a steady increase in growth rate since 1964,” the study authors write, “and the growth rate of modern broilers is now three times higher than that of the red junglefowl.”

So the next time you tuck into a plate of drumsticks or wings, remember: archaeologists of the future may one day be able to find and identify your meal.

Ant Colonies Retain Memories That Outlast the Lifespans of Individuals

An ant colony can thrive for decades, changing its behavior based on past events even as individual ants die off every year or so

Signals from other workers can tell ants when and where to fan out and search for food.

Like a brain, an ant colony operates without central control. Each is a set of interacting individuals, either neurons or ants, using simple chemical interactions that in the aggregate generate their behavior. People use their brains to remember. Can ant colonies do that? This question leads to another question: what is memory? For people, memory is the capacity to recall something that happened in the past. We also ask computers to reproduce past actions – the blending of the idea of the computer as brain and brain as computer has led us to take ‘memory’ to mean something like the information stored on a hard drive. We know that our memory relies on changes in how much a set of linked neurons stimulate each other; that it is reinforced somehow during sleep; and that recent and long-term memory involve different circuits of connected neurons. But there is much we still don’t know about how those neural events come together, whether there are stored representations that we use to talk about something that happened in the past, or how we can keep performing a previously learned task such as reading or riding a bicycle.

Any living being can exhibit the simplest form of memory, a change due to past events. Look at a tree that has lost a branch. It remembers by how it grows around the wound, leaving traces in the pattern of the bark and the shape of the tree. You might be able to describe the last time you had the flu, or you might not. Either way, in some sense your body ‘remembers,’ because some of your cells now have different antibodies, molecular receptors, which fit that particular virus.

Past events can alter the behavior of both individual ants and ant colonies. Individual carpenter ants offered a sugar treat remembered its location for a few minutes; they were likely to return to where the food had been. Another species, the Sahara Desert ant, meanders around the barren desert, searching for food. It appears that an ant of this species can remember how far it walked, or how many steps it took, since the last time it was at the nest.

A red wood ant colony remembers its trail system leading to the same trees, year after year, although no single ant does. In the forests of Europe, they forage in high trees to feed on the excretions of aphids that in turn feed on the tree. Their nests are enormous mounds of pine needles situated in the same place for decades, occupied by many generations of colonies. Each ant tends to take the same trail day after day to the same tree. During the long winter, the ants huddle together under the snow. The Finnish myrmecologist Rainer Rosengren showed that when the ants emerge in the spring, an older ant goes out with a young one along the older ant’s habitual trail. The older ant dies and the younger ant adopts that trail as its own, thus leading the colony to remember, or reproduce, the previous year’s trails.

Foraging in a harvester ant colony requires some individual ant memory. The ants search for scattered seeds and do not use pheromone signals; if an ant finds a seed, there is no point recruiting others because there are not likely to be other seeds nearby. The foragers travel a trail that can extend up to 20 meters from the nest. Each ant leaves the trail and goes off on its own to search for food. It searches until it finds a seed, then goes back to the trail, maybe using the angle of the sunlight as a guide, to return to the nest, following the stream of outgoing foragers. Once back at the nest, a forager drops off its seed, and is stimulated to leave the nest by the rate at which it meets other foragers returning with food. On its next trip, it leaves the trail at about the same place to search again.

Every morning, the shape of the colony’s foraging area changes, like an amoeba that expands and contracts. No individual ant remembers the colony’s current place in this pattern. On each forager’s first trip, it tends to go out beyond the rest of the other ants travelling in the same direction. The result is in effect a wave that reaches further as the day progresses. Gradually the wave recedes, as the ants making short trips to sites near the nest seem to be the last to give up.

From day to day, the colony’s behavior changes, and what happens on one day affects the next. I conducted a series of perturbation experiments. I put out toothpicks that the workers had to move away, or blocked the trails so that foragers had to work harder, or created a disturbance that the patrollers tried to repel. Each experiment affected only one group of workers directly, but the activity of other groups of workers changed, because workers of one task decide whether to be active depending on their rate of brief encounters with workers of other tasks. After just a few days repeating the experiment, the colonies continued to behave as they did while they were disturbed, even after the perturbations stopped. Ants had switched tasks and positions in the nest, and so the patterns of encounter took a while to shift back to the undisturbed state. No individual ant remembered anything but, in some sense, the colony did.

Colonies live for 20-30 years, the lifetime of the single queen who produces all the ants, but individual ants live at most a year. In response to perturbations, the behavior of older, larger colonies is more stable than that of younger ones. It is also more homeostatic: the larger the magnitude of the disturbance, the more likely older colonies were to focus on foraging than on responding to the hassles I had created; while, the worse it got, the more the younger colonies reacted. In short, older, larger colonies grow up to act more wisely than younger smaller ones, even though the older colony does not have older, wiser ants.

Ants use the rate at which they meet and smell other ants, or the chemicals deposited by other ants, to decide what to do next. A neuron uses the rate at which it is stimulated by other neurons to decide whether to fire. In both cases, memory arises from changes in how ants or neurons connect and stimulate each other. It is likely that colony behavior matures because colony size changes the rates of interaction among ants. In an older, larger colony, each ant has more ants to meet than in a younger, smaller one, and the outcome is a more stable dynamic. Perhaps colonies remember a past disturbance because it shifted the location of ants, leading to new patterns of interaction, which might even reinforce the new behavior overnight while the colony is inactive, just as our own memories are consolidated during sleep. Changes in colony behavior due to past events are not the simple sum of ant memories, just as changes in what we remember, and what we say or do, are not a simple set of transformations, neuron by neuron. Instead, your memories are like an ant colony’s: no particular neuron remembers anything although your brain does.Aeon counter – do not remove

Sharks Have Existed More or Less Unchanged For Millions of Years, Until Now

Shark populations off the east coast of Australia have been declining over the past 55 years with little sign of recovery, according to research published in the journal Communications Biology.

Coastal shark numbers are continuing a 50-year decline, contradicting popular theories of exploding shark populations, according to an analysis of Queensland Shark Control Program data.

University of Queensland and Griffith University researchers analysed data from the program, which has used baited drumlines and nets since 1962 to and now covers 1,760 km of the Queensland coastline.

Chris Brown from Griffith’s Australian Rivers Institute says the results show consistent and widespread declines of apex sharks — tiger white sharks and hammerheads — along Queensland’s coastline.

main article image

“We were surprised at how rapid these declines were, especially in the early years of the shark control program. We had to use specialist statistical methods to properly estimate the declines, because they occurred so quickly,” says Brown.

“We were also surprised to find the declines were so consistent across different regions.”

Some species, such as hammerhead sharks, were recognised internationally as being at risk of extinction.

“Sharks are an important part of Australia’s identity. They are also survivors that have been around for hundreds of millions of years, surviving through the extinction of dinosaurs,” he says.

“It would be a great tragedy if we lost these species because of preventable human causes.

“Sharks play important roles in ecosystems as scavengers and predators, and they are indicators of healthy ecosystems. These declines are concerning because they suggest the health of coastal ecosystems is also declining.”

George Roff, a UQ School of Biological Sciences researcher, says historical baselines of Queensland shark populations are largely unknown despite a long history of shark exploitation by recreational and commercial fisheries.

“Explorers in the 19th century once described Australian coastlines as being chock-full of sharks, yet we don’t have a clear idea of how many sharks there used to be on Queensland beaches,” he says.

“Shark populations around the world have declined substantially in recent decades, with many species being listed as vulnerable and endangered.”

Researchers discuss their findings:

The research team reconstructed historical records of shark catches to explore changes in the number and sizes of sharks over the past half century.

“What we found is that large apex sharks such as hammerheads, tigers and white sharks, have declined by 74 to 92 per cent along Queensland’s coast,” Roff says.

“And the chance of zero catch – catching no sharks at any given beach per year – has increased by as much as seven-fold.

“The average size of sharks has also declined – tiger sharks and hammerhead sharks are getting smaller.”

“We will never know the exact numbers of sharks in our oceans more than half a century ago, but the data points to radical changes in our coastal ecosystems since the 1960s.

“The data acts as a window into the past, revealing what was natural on our beaches, and provides important context for how we manage sharks.

“What may appear to be increases in shark numbers is in reality a fraction of past baselines, and the long-term trend shows ongoing declines.

“While often perceived as a danger to the public, sharks play important ecological roles in coastal ecosystems.

“Large apex sharks are able to prey on larger animals such as turtles, dolphins and dugongs, and their widespread movement patterns along the coastline connects coral reefs, seagrass beds and coastal ecosystems.

“Such losses of apex sharks is likely to have changed the structure of coastal food webs over the past half century.”

Most Biology Textbooks Overlook The Most Abundant Animals on The Planet

Insects are kind of a big deal. As many as 30 million species make up this ecologically important class, only a fraction of which we know about. Around 80 percent of all animal species are insects. Estimates put their numbers in the quintillions.

Not that you’d easily know that if you opened a random introductory biology textbook – these are much more likely to give vertebrates a starring role. So it might be time to put the spineless members of the animal kingdom back into the spotlight.

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A recent survey of 88 popular entry-level texts published between 1906 and 2016 found insects just weren’t filling the pages in a way that reflected not just their abundance, but their significance in ecology.

“Insects are essential to every terrestrial ecosystem and play important roles in everything from agriculture to human health,” says North Carolina State University biologist Jennifer Landin.

“But our analysis shows that students taking entry-level biology courses are learning virtually nothing about them.”

Most surprisingly, this deficit has been on the increase since the 1960s. Our interest in the humble bug just isn’t what it used to be. And that’s a problem, according to the researchers.

“We do not exist apart from nature,” Landin says.

“Humans and insects, for example, have direct effects on each other – and that is no longer clearly presented in the teaching literature.”

To explore how generalised biology textbooks have changed over time with respect to their choice of content, the researchers combed their selection of textbooks for words, figures and illustrations that featured some kind of insect.

These were then recorded against the book’s year of publication, revealing a gentle slide in the percentage of textbook pages dedicated to insect anatomy, lives, and relationships.

A century ago, you could expect an average of 32.6 pages to be devoted to something insecty. That’s about 8.8 percent of the total.

Fast forward to books published between 2000 and 2016, that number drops to 5.67 pages. A miserable 0.59 percent.

As if that’s not bad enough, the team found a huge imbalance in the categories of these super important arthropods.

Orthoptera – such as locusts and crickets – were overrepresented. They make up just 2 percent of insect species, but occupied as much as a quarter of the insect real estate.

Beetles, of the order Coleoptera, also represented about a quarter of those pages, in spite of making up a whopping 37 percent of all species of insect.

You could argue that big numbers don’t necessarily make for an important group of animals. There’s only so many pages in a textbook, and only so much time to study them all – finding the right representatives requires a little more nuance.

But in addition to a quantitative assessment, the team examined the kinds of words used to describe insects, and assigned them an emotive value as viewed by a relative entomological novice.

So while ‘pest’ might well be fairly denotative to an expert, to the average first-year student this would make an insect look less like the hero of the story.

Texts published prior to the 1960s contained 8.7 times more descriptors, of both a positive and negative variety, than those published after 2000.

However, those words tended to be a little more positive. We might not be as colourful in our descriptions today, but the occasional connotations appear to be less in the insect’s favour.

So not only are we talking about ants, moths, and flies less, we’re less likely to be flattering in our descriptors.

“We saw societal shifts in the groups of insects addressed in texts; butterflies were covered more when butterfly collecting was a popular hobby, mosquitoes and other flies were overwhelming in books when insect-transmitted disease was rampant,” says Landin.

This social relevance is to be expected in textbook trends. But far from becoming less important, a decline in insect numbers thanks to climate change is a concern we face in future decades.

We want our future biologists to be not just informed on 80 percent of all animal species, we want them to be excited by them.

It’s time to back the bugs!

How Neanderthal DNA might have shaped some human brains

Gene variants acquired through interbreeding seem to give some people with European ancestry more elongated brains.



CT scans of a neanderthal cranium (l) and a modern human (r) rotating on a black background

Neanderthal skulls (left) are more elongated than modern-human skulls.

No human has the brain of a Neanderthal — but some have hints of its shape.

The brain shape of some people with European ancestry is influenced by Neanderthal DNA acquired through interbreeding tens of thousands of years ago, researchers report on 13 December in Current Biology1.

These DNA variants seem to affect the expression of two genes in such a way as to make the brains of some humans slightly less round, and more like the Neanderthals’ elongated brains.

“It’s a really subtle shift in the overall roundedness,” says team member Philipp Gunz, a palaeoanthropologist at the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany. “I don’t think you would see it with your naked eye. These are not people that would look Neanderthal-like.”

The Neanderthal DNA variants alter gene expression in brain regions involved in planning, coordination and learning of movements. These faculties are used in speech and language, but there is no indication that the Neanderthal DNA affects cognition in modern humans.

Instead, the researchers say, their discovery points to biological changes that might have endowed the human brain with its distinct shape.

Changing brains

Earlier this year, Gunz and two colleagues determined that the rounded brain shape of modern humans evolved gradually, reaching its current appearance between 35,000 and 100,000 years ago2. The earliest human fossils from across Africa, dating to around 200,000–300,000 years ago, have large yet elongated brains. “There really is something going on in the brain that changes over time in the Homo sapiens lineage,” says Gunz.

Given that brains don’t fossilize well, looking at how Neanderthal DNA affects human biology is one of the only ways to study differences between the species, says Tony Capra, an evolutionary geneticist at Vanderbilt University in Nashville, Tennessee. “We’re never going to be able to dig up a Neanderthal brain intact and compare it to our brain,” he says.

So the team set out to identify DNA variants that contributed to humans’ rounded brains. They hypothesized that some Neanderthal variants — which all humans with Eurasian ancestry carry — might affect H. sapiens brain shape and make it more elongated.

They first analysed brain scans from 4,468 people of European ancestry, and quantified their overall roundedness. The researchers then tested whether any of about 50,000 Neanderthal DNA variants known to occur in some modern humans were associated with difference in their brain shape.

They pinpointed variants near two genes. The variants don’t alter the shape of the proteins those genes encode — but rather, where in the brain they are made.

Neanderthal variants near a gene called UBR4, which has a role in making neurons, reduce that gene’s expression in deep brain structures called the basal ganglia.

People with a Neanderthal variant near a gene called PHLPP1, which is involved in building the fatty sheaths that insulate nerves, have greater expression of that gene in their cerebellums.

Important function

Cedric Boeckx, a neuroscientist at the Catalan Institute for Research and Advanced Studies in Barcelona, Spain, is intrigued by the brain regions in which expression of these genes is altered, which have previously been linked to human cognition, and to either the absence of or suppression of Neanderthal genes.

For example, a 2017 study found that the expression of Neanderthal genes tends to be suppressed in the basal ganglia and cerebellum3, suggesting that the human versions of the genes are important to brain function.

Speech and tool use are also likely to depend on exquisite motor control underpinned by these regions, notes Simon Fisher, a neurogeneticist at the Max Planck Institute for Psycholinguistics in Nijmegen, the Netherlands, who led the latest study.

Fisher co-discovered a gene implicated in language, FOXP2, that influences brain circuits in the basal ganglia and cerebellum. A study in 2014 found that FOXP2 lies in a large swathe of the human genome that contains no Neanderthal variants.4

Yet the researchers say there is no evidence that the variants they identified in the new study affect language or any trait other than brain shape.

And, both Fisher and Boeckx say that many more genes, active in different parts of brain, probably also affect the brain’s roundedness. “We don’t see this as something where this is a single gene that magically changed brain shape,” Fisher says.

Next, his team plans to look for more variants that affect this trait in the UK Biobank database, which is gathering brain scans and genome data for 100,000 people. “We need to go and find more of these genes,” he says.

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