Are there any bacteria that can receive ultrasound signals?

Are there any bacteria that can receive ultrasound signals?

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I'm looking for an example of bacteria that could receive ultrasound (at any ultrasound frequency) signal and possibly perform some (re)action afterwards.

I'm going to post a quick answer here, really a thought piece.

Usually to detect a sound wave you need a sounding board about the wavelength of the sound.

Bacteria are on the order of a few microns in length.

Ultrasound frequencies range from 2 to 200 MHz (and up I assume).

To have a wavelength on the order of 3 microns, a 100 MHz wave would be needed.

So only on the very high end of the range. If bacteria make sound though, they probably are on this frequency range.

I wonder if this has been looked at? Not sure it has. While in biology you never say never - if a bacterium really needs to pick up a wave it might have a clever adaptation to do so, but in the 100MHz + frequency range seems more likely.

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Ultrasound in Medicine and Biology is the official journal of the World Federation for Ultrasound in Medicine and Biology. The journal publishes original contributions that demonstrate a novel application of an existing ultrasound technology in clinical diagnostic, interventional and therapeutic applications, new and improved clinical techniques, the physics, engineering and technology of ultrasound in medicine and biology, and the interactions between ultrasound and biological systems, including bioeffects. Papers that simply utilize standard diagnostic ultrasound as a measuring tool will be considered out of scope. Extended critical reviews of subjects of contemporary interest in the field are also published, in addition to occasional editorial articles, clinical and technical notes, book reviews, letters to the editor and a calendar of forthcoming meetings. It is the aim of the journal fully to meet the information and publication requirements of the clinicians, scientists, engineers and other professionals who constitute the biomedical ultrasonic community.

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Silicon chips combine light and ultrasound for better signal processing

Credit: Pixabay/CC0 Public Domain

The continued growth of wireless and cellular data traffic relies heavily on light waves. Microwave photonics is the field of technology that is dedicated to the distribution and processing of electrical information signals using optical means. Compared with traditional solutions based on electronics alone, microwave photonic systems can handle massive amounts of data. Therefore, microwave photonics has become increasingly important as part of 5G cellular networks and beyond. A primary task of microwave photonics is the realization of narrowband filters: The selection of specific data, at specific frequencies, out of immense volumes that are carried over light.

Many microwave photonic systems are built of discrete, separate components and long optical fiber paths. However, the cost, size, power consumption and production volume requirements of advanced networks call for a new generation of microwave photonic systems that are realized on a chip. Integrated microwave photonic filters, particularly in silicon, are highly sought after. There is, however, a fundamental challenge: Narrowband filters require that signals are delayed for comparatively long durations as part of their processing.

"Since the speed of light is so fast," says Prof. Avi Zadok from Bar-Ilan University, Israel, "we run out of chip space before the necessary delays are accommodated. The required delays may reach over 100 nanoseconds. Such delays may appear to be short considering daily experience however, the optical paths that support them are over ten meters long. We cannot possibly fit such long paths as part of a silicon chip. Even if we could somehow fold over that many meters in a certain layout, the extent of optical power losses to go along with it would be prohibitive."

These long delays require a different type of wave, one that travels much more slowly. In a study recently published in the journal Optica, Zadok and his team from the Faculty of Engineering and Institute of Nanotechnology and Advanced Materials at Bar-Ilan University, and collaborators from the Hebrew University of Jerusalem and Tower Semiconductors, suggest a solution. They brought together light and ultrasonic waves to realize ultra-narrow filters of microwave signals, in silicon integrated circuits. The concept allows large freedom for filters design.

Bar-Ilan University doctoral student Moshe Katzman explains, "We've learned how to convert the information of interest from the form of light waves to ultrasonic, surface acoustic waves, and then back to optics. The surface acoustic waves travel at a speed that is 100,000 slower. We can accommodate the delays that we need as part of our silicon chip, within less than a millimeter, and with losses that are very reasonable."

Acoustic waves have served for the processing of information for sixty years however, their chip-level integration alongside light waves has proven tricky. Moshe Katzman continues, "Over the last decade we have seen landmark demonstrations of how light and ultrasound waves can be brought together on a chip device, to make up excellent microwave photonic filters. However, the platforms used were more specialized. Part of the appeal of the solution is in its simplicity. The fabrication of devices is based on routine protocols of silicon waveguides. We are not doing anything fancy here." The realized filters are very narrowband: The spectral width of the filters passbands is only 5 MHz.

In order to realize narrowband filters, the information-carrying surface acoustic waves is imprinted upon the output light wave multiple times. Doctoral student Maayan Priel elaborates, "The acoustic signal crosses the light path up to 12 times, depending on choice of layout. Each such event imprints a replica of our signal of interest on the optical wave. Due to the slow acoustic speed, these events are separated by long delays. Their overall summation is what makes the filters work." As part of their research, the team reports complete control over each replica, towards the realization of arbitrary filter responses. Maayan Priel concludes, "The freedom to design the response of the filters is making the most out of the integrated, microwave-photonic platform."


The term echolocation was coined by the American zoologist Donald Griffin, who worked with Robert Galambos was the first to convincingly demonstrate its existence in bats in 1938. [3] [4] As Griffin described in his book, [5] the 18th century Italian scientist Lazzaro Spallanzani had, by means of a series of elaborate experiments, concluded that when bats fly at night, they rely on some sense besides vision, but he did not discover that the other sense was hearing. [6] [7] The Swiss physician and naturalist Louis Jurine repeated Spallanzani's experiments (using different species of bat), and concluded that when bats hunt at night, they rely on hearing. [8] [9] [10] In 1908, Walter Louis Hahn confirmed Spallanzani's and Jurine's findings. [11]

In 1912, the inventor Hiram Maxim independently proposed that bats used sound below the human auditory range to avoid obstacles. [12] In 1920, the English physiologist Hamilton Hartridge correctly proposed instead that bats used frequencies above the range of human hearing. [13] [14]

Echolocation in odontocetes (toothed whales) was not properly described until two decades after Griffin and Galambos' work, by Schevill and McBride in 1956. [15] However, in 1953, Jacques Yves Cousteau suggested in his first book, The Silent World (pp. 206–207) that porpoises had something like sonar, judging by their navigational abilities.

Echolocation is the same as active sonar, using sounds made by the animal itself. Ranging is done by measuring the time delay between the animal's own sound emission and any echoes that return from the environment. The relative intensity of sound received at each ear as well as the time delay between arrival at the two ears provide information about the horizontal angle (azimuth) from which the reflected sound waves arrive. [16]

Unlike some human-made sonars that rely on many extremely narrow beams and many receivers to localize a target (multibeam sonar), animal echolocation has only one transmitter and two receivers (the ears) positioned slightly apart. The echoes returning to the ears arrive at different times and at different intensities, depending on the position of the object generating the echoes. The time and loudness differences are used by the animals to perceive distance and direction. With echolocation, the bat or other animal can see not only where it is going but also how big another animal is, what kind of animal it is, and other features. [17] [18]

At the most basic level, echolocation is based on the neural anatomy of auditory brain circuitry. In essence, ascending brain pathways in the brain stem allow the brain to calculate the difference between the two ears to very small fractions of a second. [19]

Echolocating bats use echolocation to navigate and forage, often in total darkness. They generally emerge from their roosts in caves, attics, or trees at dusk and hunt for insects into the night. Using echolocation, bats can determine how far away an object is, the object's size, shape and density, and the direction (if any) that an object is moving. Their use of echolocation allows them to occupy a niche where there are often many insects (that come out at night since there are fewer predators then), less competition for food, and fewer species that may prey on the bats themselves. [20]

Echolocating bats generate ultrasound via the larynx and emit the sound through the open mouth or, much more rarely, the nose. [21] The latter is most pronounced in the horseshoe bats (Rhinolophus spp.). Bat echolocation calls range in frequency from 14,000 to well over 100,000 Hz, mostly beyond the range of the human ear (typical human hearing range is considered to be from 20 Hz to 20,000 Hz). Bats may estimate the elevation of targets by interpreting the interference patterns caused by the echoes reflecting from the tragus, a flap of skin in the external ear. [22]

There are two hypotheses about the evolution of echolocation in bats. The first suggests that laryngeal echolocation evolved twice in Chiroptera, once in the Yangochiroptera and once in the horseshoe bats (Rhinolophidae). [23] [24] The second proposes that laryngeal echolocation had a single origin in Chiroptera, was subsequently lost in the family Pteropodidae, and later evolved as a system of tongue-clicking in the genus Rousettus. [25]

Individual bat species echolocate within specific frequency ranges that suit their environment and prey types. This has sometimes been used by researchers to identify bats flying in an area simply by recording their calls with ultrasonic recorders known as "bat detectors". However echolocation calls are not always species specific and some bats overlap in the type of calls they use so recordings of echolocation calls cannot be used to identify all bats. In recent years researchers in several countries have developed "bat call libraries" that contain recordings of local bat species that have been identified known as "reference calls" to assist with identification. [26] [27] [28]

Since the 1970s there has been an ongoing controversy among researchers as to whether bats use a form of processing known from radar termed coherent cross-correlation. Coherence means that the phase of the echolocation signals is used by the bats, while cross-correlation just implies that the outgoing signal is compared with the returning echoes in a running process. Today most – but not all – researchers believe that they use cross-correlation, but in an incoherent form, termed a filter bank receiver. [ citation needed ]

When searching for prey they produce sounds at a low rate (10–20 clicks/second). During the search phase the sound emission is coupled to respiration, which is again coupled to the wingbeat. This coupling appears to dramatically conserve energy as there is little to no additional energetic cost of echolocation to flying bats. [29] After detecting a potential prey item, echolocating bats increase the rate of pulses, ending with the terminal buzz, at rates as high as 200 clicks/second. During approach to a detected target, the duration of the sounds is gradually decreased, as is the energy of the sound. [30]

Calls and ecology Edit

Echolocating bats occupy a diverse set of ecological conditions – they can be found living in environments as different as Europe and Madagascar, and hunting for food sources as different as insects, frogs, nectar, fruit, and blood. Additionally, the characteristics of an echolocation call are adapted to the particular environment, hunting behavior, and food source of the particular bat. However, this adaptation of echolocation calls to ecological factors is constrained by the phylogenetic relationship of the bats, leading to a process known as descent with modification, and resulting in the diversity of the Chiroptera today. [31] [32] [33] [34] [35] [36]

Flying insects are a common source of food for echolocating bats and some insects (moths in particular) can hear the calls of predatory bats. There is evidence that moth hearing has evolved in response to bat echolocation to avoid capture. [37] Furthermore, these moth adaptations provide selective pressure for bats to improve their insect-hunting systems and this cycle culminates in a moth-bat "evolutionary arms race." [38] [39]

Acoustic features Edit

Describing the diversity of bat echolocation calls requires examination of the frequency and temporal features of the calls. It is the variations in these aspects that produce echolocation calls suited for different acoustic environments and hunting behaviors. [31] [33] [36] [40] [41]

Frequency Edit

Bat call frequencies range from as low as 11 kHz to as high as 212 kHz. [42] Insectivorous aerial-hawking bats have a call frequency between 20 kHz and 60 kHz because it is the frequency that gives the best range and image acuity and makes them less conspicuous to insects. [43] However, low frequencies are adaptive for some species with different prey and environments. Euderma maculatum, a species that feeds on moths, uses a particularly low frequency of 12.7 kHz that cannot be heard by moths. [44]

Frequency modulation and constant frequency Edit

Echolocation calls can be composed of two different types of frequency structure: frequency modulated (FM) sweeps, and constant frequency (CF) tones. A particular call can consist of one, the other, or both structures. An FM sweep is a broadband signal – that is, it contains a downward sweep through a range of frequencies. A CF tone is a narrowband signal: the sound stays constant at one frequency throughout its duration. [ citation needed ]

Intensity Edit

Echolocation calls have been measured at intensities anywhere between 60 and 140 decibels. [45] Certain bat species can modify their call intensity mid-call, lowering the intensity as they approach objects that reflect sound strongly. This prevents the returning echo from deafening the bat. [41] High-intensity calls such as those from aerial-hawking bats (133 dB) are adaptive to hunting in open skies. Their high intensity calls are necessary to even have moderate detection of surroundings because air has a high absorption of ultrasound and because insects' size only provide a small target for sound reflection. [46] Additionally, the so-called "whispering bats" have adapted low-amplitude echolocation so that their prey, moths, which are able to hear echolocation calls, are less able to detect and avoid an oncoming bat. [44]

Harmonic composition Edit

Calls can be composed of one frequency or multiple frequencies comprising a harmonic series. In the latter case, the call is usually dominated by a certain harmonic ("dominant" frequencies are those present at higher intensities than other harmonics present in the call). [ citation needed ]

Call duration Edit

A single echolocation call (a call being a single continuous trace on a sound spectrogram, and a series of calls comprising a sequence or pass) can last anywhere from 0.2 to 100 milliseconds in duration, depending on the stage of prey-catching behavior that the bat is engaged in. For example, the duration of a call usually decreases when the bat is in the final stages of prey capture – this enables the bat to call more rapidly without overlap of call and echo. Reducing duration comes at the cost of having less total sound available for reflecting off objects and being heard by the bat. [42]

Pulse interval Edit

The time interval between subsequent echolocation calls (or pulses) determines two aspects of a bat's perception. First, it establishes how quickly the bat's auditory scene information is updated. For example, bats increase the repetition rate of their calls (that is, decrease the pulse interval) as they home in on a target. This allows the bat to get new information regarding the target's location at a faster rate when it needs it most. Secondly, the pulse interval determines the maximum range that bats can detect objects. This is because bats can only keep track of the echoes from one call at a time as soon as they make another call they stop listening for echoes from the previously made call. For example, a pulse interval of 100 ms (typical of a bat searching for insects) allows sound to travel in air roughly 34 meters so a bat can only detect objects as far away as 17 meters (the sound has to travel out and back). With a pulse interval of 5 ms (typical of a bat in the final moments of a capture attempt), the bat can only detect objects up to 85 cm away. Therefore, the bat constantly has to make a choice between getting new information updated quickly and detecting objects far away. [47]

FM signal advantages Edit

The major advantage conferred by an FM signal is extremely precise range discrimination, or localization, of the target. J.A. Simmons demonstrated this effect with a series of elegant experiments that showed how bats using FM signals could distinguish between two separate targets even when the targets were less than half a millimeter apart. This ability is due to the broadband sweep of the signal, which allows for better resolution of the time delay between the call and the returning echo, thereby improving the cross correlation of the two. Additionally, if harmonic frequencies are added to the FM signal, then this localization becomes even more precise. [31] [32] [33] [36]

One possible disadvantage of the FM signal is a decreased operational range of the call. Because the energy of the call is spread out among many frequencies, the distance at which the FM-bat can detect targets is limited. [34] This is in part because any echo returning at a particular frequency can only be evaluated for a brief fraction of a millisecond, as the fast downward sweep of the call does not remain at any one frequency for long. [32]

CF signal advantages Edit

The structure of a CF signal is adaptive in that it allows the CF-bat to detect both the velocity of a target, and the fluttering of a target's wings as Doppler shifted frequencies. A Doppler shift is an alteration in sound wave frequency, and is produced in two relevant situations: when the bat and its target are moving relative to each other, and when the target's wings are oscillating back and forth. CF-bats must compensate for Doppler shifts, lowering the frequency of their call in response to echoes of elevated frequency – this ensures that the returning echo remains at the frequency to which the ears of the bat are most finely tuned. The oscillation of a target's wings also produces amplitude shifts, which gives a CF-bat additional help in distinguishing a flying target from a stationary one. [48] [33] [36] [32] [35] [31]

Additionally, because the signal energy of a CF call is concentrated into a narrow frequency band, the operational range of the call is much greater than that of an FM signal. This relies on the fact that echoes returning within the narrow frequency band can be summed over the entire length of the call, which maintains a constant frequency for up to 100 milliseconds. [32] [34]

Acoustic environments of FM and CF signals Edit

A frequency modulated (FM) component is excellent for hunting prey while flying in close, cluttered environments. Two aspects of the FM signal account for this fact: the precise target localization conferred by the broadband signal, and the short duration of the call. The first of these is essential because in a cluttered environment, the bats must be able to resolve their prey from large amounts of background noise. The 3D localization abilities of the broadband signal enable the bat to do exactly that, providing it with what Simmons and Stein (1980) call a "clutter rejection strategy." This strategy is further improved by the use of harmonics, which, as previously stated, enhance the localization properties of the call. The short duration of the FM call is also best in close, cluttered environments because it enables the bat to emit many calls extremely rapidly without overlap. This means that the bat can get an almost continuous stream of information – essential when objects are close, because they will pass by quickly – without confusing which echo corresponds to which call. [35] [36] [31] [34]

A constant frequency (CF) component is often used by bats hunting for prey while flying in open, clutter-free environments, or by bats that wait on perches for their prey to appear. The success of the former strategy is due to two aspects of the CF call, both of which confer excellent prey-detection abilities. First, the greater working range of the call allows bats to detect targets present at great distances – a common situation in open environments. Second, the length of the call is also suited for targets at great distances: in this case, there is a decreased chance that the long call will overlap with the returning echo. The latter strategy is made possible by the fact that the long, narrowband call allows the bat to detect Doppler shifts, which would be produced by an insect moving either towards or away from a perched bat. [35] [36] [31] [34]

Neural mechanisms Edit

Because bats use echolocation to orient themselves and to locate objects, their auditory systems are adapted for this purpose, highly specialized for sensing and interpreting the stereotyped echolocation calls characteristic of their own species. This specialization is evident from the inner ear up to the highest levels of information processing in the auditory cortex. [49]

Inner ear and primary sensory neurons Edit

Both CF and FM bats have specialized inner ears which allow them to hear sounds in the ultrasonic range, far outside the range of human hearing. Although in most other aspects, the bat's auditory organs are similar to those of most other mammals, certain bats (horseshoe bats, Rhinolophus spp. and the moustached bat, Pteronotus parnelii) with a constant frequency (CF) component to their call (known as high duty cycle bats) do have a few additional adaptations for detecting the predominant frequency (and harmonics) of the CF vocalization. These include a narrow frequency "tuning" of the inner ear organs, with an especially large area responding to the frequency of the bat's returning echoes. [35]

The basilar membrane within the cochlea contains the first of these specializations for echo information processing. In bats that use CF signals, the section of the membrane that responds to the frequency of returning echoes is much larger than the region of response for any other frequency. For example, in the greater horseshoe bat, Rhinolophus ferrumequinum, there is a disproportionately lengthened and thickened section of the membrane that responds to sounds around 83 kHz, the constant frequency of the echo produced by the bat's call. This area of high sensitivity to a specific, narrow range of frequency is known as an "acoustic fovea". [50]

Odontocetes (toothed whales and dolphins) have similar cochlear specializations to those found in bats. Odontocetes also have the highest neural investment of any cochleae reported to date with ratios of greater than 1500 ganglion cells/mm of basilar membrane. [ citation needed ]

Further along the auditory pathway, the movement of the basilar membrane results in the stimulation of primary auditory neurons. Many of these neurons are specifically "tuned" (respond most strongly) to the narrow frequency range of returning echoes of CF calls. Because of the large size of the acoustic fovea, the number of neurons responding to this region, and thus to the echo frequency, is especially high. [51]

Inferior colliculus Edit

In the Inferior colliculus, a structure in the bat's midbrain, information from lower in the auditory processing pathway is integrated and sent on to the auditory cortex. As George Pollak and others showed in a series of papers in 1977, the interneurons in this region have a very high level of sensitivity to time differences, since the time delay between a call and the returning echo tells the bat its distance from the target object. While most neurons respond more quickly to stronger stimuli, collicular neurons maintain their timing accuracy even as signal intensity changes. [ citation needed ]

These interneurons are specialized for time sensitivity in several ways. First, when activated, they generally respond with only one or two action potentials. This short duration of response allows their action potentials to give a very specific indication of the exact moment of the time when the stimulus arrived, and to respond accurately to stimuli that occur close in time to one another. In addition, the neurons have a very low threshold of activation – they respond quickly even to weak stimuli. Finally, for FM signals, each interneuron is tuned to a specific frequency within the sweep, as well as to that same frequency in the following echo. There is specialization for the CF component of the call at this level as well. The high proportion of neurons responding to the frequency of the acoustic fovea actually increases at this level. [33] [51] [52]

Auditory cortex Edit

The auditory cortex in bats is quite large in comparison with other mammals. [53] Various characteristics of sound are processed by different regions of the cortex, each providing different information about the location or movement of a target object. Most of the existing studies on information processing in the auditory cortex of the bat have been done by Nobuo Suga on the mustached bat, Pteronotus parnellii. This bat's call has both CF tone and FM sweep components.

Suga and his colleagues have shown that the cortex contains a series of "maps" of auditory information, each of which is organized systematically based on characteristics of sound such as frequency and amplitude. The neurons in these areas respond only to a specific combination of frequency and timing (sound-echo delay), and are known as combination-sensitive neurons.

The systematically organized maps in the auditory cortex respond to various aspects of the echo signal, such as its delay and its velocity. These regions are composed of "combination sensitive" neurons that require at least two specific stimuli to elicit a response. The neurons vary systematically across the maps, which are organized by acoustic features of the sound and can be two dimensional. The different features of the call and its echo are used by the bat to determine important characteristics of their prey. The maps include:

  • FM-FM area: This region of the cortex contains FM-FM combination-sensitive neurons. These cells respond only to the combination of two FM sweeps: a call and its echo. The neurons in the FM-FM region are often referred to as "delay-tuned," since each responds to a specific time delay between the original call and the echo, in order to find the distance from the target object (the range). Each neuron also shows specificity for one harmonic in the original call and a different harmonic in the echo. The neurons within the FM-FM area of the cortex of Pteronotus are organized into columns, in which the delay time is constant vertically but increases across the horizontal plane. The result is that range is encoded by location on the cortex, and increases systematically across the FM-FM area. [35][51][54][55]
  • CF-CF area: Another kind of combination-sensitive neuron is the CF-CF neuron. These respond best to the combination of a CF call containing two given frequencies – a call at 30 kHz (CF1) and one of its additional harmonics around 60 or 90 kHz (CF2 or CF3) – and the corresponding echoes. Thus, within the CF-CF region, the changes in echo frequency caused by the Doppler shift can be compared to the frequency of the original call to calculate the bat's velocity relative to its target object. As in the FM-FM area, information is encoded by its location within the map-like organization of the region. The CF-CF area is first split into the distinct CF1-CF2 and CF1-CF3 areas. Within each area, the CF1 frequency is organized on an axis, perpendicular to the CF2 or CF3 frequency axis. In the resulting grid, each neuron codes for a certain combination of frequencies that is indicative of a specific velocity [51][54][56]
  • Doppler shifted constant frequency (DSCF) area: This large section of the cortex is a map of the acoustic fovea, organized by frequency and by amplitude. Neurons in this region respond to CF signals that have been Doppler shifted (in other words, echoes only) and are within the same narrow frequency range to which the acoustic fovea responds. For Pteronotus, this is around 61 kHz. This area is organized into columns, which are arranged radially based on frequency. Within a column, each neuron responds to a specific combination of frequency and amplitude. Suga's studies have indicated that this brain region is necessary for frequency discrimination. [51][54][56]

Biosonar is valuable to toothed whales (suborder Odontoceti), including dolphins, porpoises, river dolphins, killer whales and sperm whales, because they live in an underwater habitat that has favourable acoustic characteristics and where vision is extremely limited in range due to absorption or turbidity. [ citation needed ]

Cetacean evolution consisted of three main radiations. Throughout the middle and late Eocene periods (49-31.5 million years ago), archaeocetes, primitive toothed Cetacea that arose from terrestrial mammals with the creation of aquatic adaptations, were the only known archaic Cetacea. [57] These primitive aquatic mammals did not possess the ability to echolocate, although they did have slightly adapted underwater hearing. [58] The morphology of acoustically isolated ear bones in basilosaurid archaeocetes indicates that this order had directional hearing underwater at low to mid frequencies by the late middle Eocene. [59] However, with the extinction of archaeocete at the onset of the Oligocene, two new lineages in the early Oligocene period (31.5-28 million years ago) comprised a second radiation. These early mysticetes (baleen whales) and odontocetes can be dated back to the middle Oligocene in New Zealand. [57] Based on past phylogenies, it has been found that the evolution of extant odontocetes is monophyletic however, echolocation evolved twice, convergently, along the odontocete lineage: once in Xenorophus, and oligocene stem odontocete, and once in the crown odontecete [60] Dispersal rates routes of early odontocetes included transoceanic travel to new adaptive zones. The third radiation occurred later in the Neogene, when present dolphins and their relatives evolved to be the most common species in the modern sea. [58] The evolution of echolocation could be attributed to several theories. There are two proposed drives for the hypotheses of cetacean radiation, one biotic and the other abiotic in nature. The first, adaptive radiation, is the result of a rapid divergence into new adaptive zones. This results in diverse, ecologically different clades that are incomparable. [61] Clade Neocete (crown cetacean) has been characterized by an evolution from archaeocetes and a dispersion across the world's oceans, and even estuaries and rivers. These ecological opportunities were the result of abundant dietary resources with low competition for hunting. [62] This hypothesis of lineage diversification, however, can be unconvincing due to a lack of support for rapid speciation early in cetacean history. A second, more abiotic drive is better supported. Physical restructuring of the oceans has played a role in echolocation radiation. This was a result of global climate change at the Eocene-Oligocene boundary from a greenhouse to an icehouse world. Tectonic openings created the emergence of the Southern ocean with a free flowing Antarctic Circumpolar current. [57] [58] [59] [62] These events allowed for a selection regime characterized by the ability to locate and capture prey in turbid river waters, or allow odontocetes to invade and feed at depths below the photic zone. Further studies have found that echolocation below the photic zone could have been a predation adaptation to diel migrating cephalopods. [59] [63] Since its advent, there has been adaptive radiation especially in the family Delphinidae (dolphins) in which echolocation has become extremely derived. [64]

Two proteins have been found to play a major role in toothed whale echolocation. Prestin, a motor protein of the outer hair cells of the inner ear of the mammalian cochlea, has an association between the number of nonsynonymous substitutions and hearing sensitivity. [65] It has undergone two clear episodes of accelerated protein evolution in cetaceans: on the ancestral branch of odontocetes and on the branch leading to delphinioidae. [65] The first episode of acceleration is connected to odontocete divergence, when echolocation first developed, and the second occurs with the increase in echolocation frequency seen in the family Delphinioidae. Cldn14, a member of the tight junction proteins which form barriers between inner ear cells, shows exactly the same evolutionary pattern as Prestin. [66] The two events of protein evolution, for Prestin and Cldn14, occurred at the same times as the tectonic opening of the Drake Passage (34-31 Ma) and the Antarctic ice growth at the middle Miocene climate transition (14 Ma), with the divergence of odontocetes and mysticetes occurring with the former, and the speciation of delphinioidae with the latter. [62] There is a strong connection between these proteins, the ocean restructuring events, and the echolocation evolution.

Thirteen species of extant odontocete evolved narrow-band high-frequency (NBHF) echolocation in four separate, convergent events. These species include the families Kogiidae (pygmy sperm whales) and Phocoenidae (porpoises), as well as some species of the genus Lagenorynchus, all of Cephalorynchus, and the La Plata Dolphin. NBHF is thought to have evolved as a means of predator evasion NBHF-producing species are small relative to other odontocetes, making them viable prey to large species such as the killer whale(Orcinus orca). However, because three of the groups developed NBHF prior to the emergence of the orca, predation by other, ancient, raptorial odontocetes must have been the driving force for the development of NBHF, not predation by the orca. Orcas, and, presumably, ancient, raptorial odontocetes such as Acrophyseter are unable to hear frequencies below 100 kHz. [67]

Another reason for variation in echolocation is habitat. For all sonar systems the limiting factor deciding whether a returning echo is detected is the echo-to-noise ratio (ENR). The ENR is given by the emitted source level (SL) plus the target strength, minus the two-way transmission loss (absorption and spreading) and the received noise. [68] Animals will adapt either to maximize range under noise-limited conditions (increase source level) or to reduce noise clutter in a shallow and/or littered habitat (decrease source level). In cluttered habitats, such as coastal areas, prey ranges are smaller, and species like Commerson's dolphin (Cephalorhynchus commersonii) have lowered source levels to better suit their environment. [68]

Toothed whales emit a focused beam of high-frequency clicks in the direction that their head is pointing. Sounds are generated by passing air from the bony nares through the phonic lips. These sounds are reflected by the dense concave bone of the cranium and an air sac at its base. The focused beam is modulated by a large fatty organ known as the 'melon'. This acts like an acoustic lens because it is composed of lipids of differing densities. Most toothed whales use clicks in a series, or click train, for echolocation, while the sperm whale may produce clicks individually. Toothed whale whistles do not appear to be used in echolocation. Different rates of click production in a click train give rise to the familiar barks, squeals and growls of the bottlenose dolphin. A click train with a repetition rate over 600 per second is called a burst pulse. In bottlenose dolphins, the auditory brain response resolves individual clicks up to 600 per second, but yields a graded response for higher repetition rates. [69]

It has been suggested that some smaller toothed whales may have their tooth arrangement suited to aid in echolocation. The placement of teeth in the jaw of a bottlenose dolphin, for example, are not symmetrical when seen from a vertical plane, and this asymmetry could possibly be an aid in the dolphin sensing if echoes from its biosonar are coming from one side or the other. [70] [71] However, this idea lacks experimental support.

Echoes are received using complex fatty structures around the lower jaw as the primary reception path, from where they are transmitted to the middle ear via a continuous fat body. Lateral sound may be received through fatty lobes surrounding the ears with a similar density to water. Some researchers believe that when they approach the object of interest, they protect themselves against the louder echo by quietening the emitted sound. In bats this is known to happen, but here the hearing sensitivity is also reduced close to a target. [72] [73]


The first known species of rhizobia, Rhizobium leguminosarum, was identified in 1889, and all further species were initially placed in the Rhizobium genus. Most research has been done on crop and forage legumes such as clover, alfalfa, beans, peas, and soybeans more research is being done on North American legumes. [ citation needed ]

Rhizobia are a paraphyletic group that fall into two classes of proteobacteria—the alphaproteobacteria and betaproteobacteria. As shown below, most belong to the order Hyphomicrobiales, but several rhizobia occur in distinct bacterial orders of the proteobacteria. [3] [4] [5]

These groups include a variety of non-symbiotic bacteria. For instance, the plant pathogen Agrobacterium is a closer relative of Rhizobium than the Bradyrhizobium that nodulate soybean (and might not really be a separate genus). [6]

Although much of the nitrogen is removed when protein-rich grain or hay is harvested, significant amounts can remain in the soil for future crops. This is especially important when nitrogen fertilizer is not used, as in organic rotation schemes or some less-industrialized countries. [7] Nitrogen is the most commonly deficient nutrient in many soils around the world and it is the most commonly supplied plant nutrient. Supply of nitrogen through fertilizers has severe environmental concerns.

Specific strains of rhizobia are required to make functional nodules on the roots able to fix the N2. [8] Having this specific rhizobia present is beneficial to the legume, as the N2 fixation can increase crop yield. [9] Inoculation with rhizobia tends to increase yield. [10]

Legume inoculation has been an agriculture practice for many years and has continuously improved over time. [9] 12–20 million hectares of soybeans are inoculated annually. The technology to produce these inoculants are microbial fermenters. An ideal inoculant includes some of the following aspects maximum efficacy, ease of use, compatibility, high rhizobial concentration, long shelf-life, usefulness under varying field conditions, and survivability. [9] [11] [12]

These inoculants may foster success in legume cultivation. [13] As a result of the nodulation process, after the harvest of the crop there are higher levels of soil nitrate, which can then be used by the next crop.

Rhizobia are unique in that they are the only nitrogen-fixing bacteria living in a symbiotic relationship with legumes. Common crop and forage legumes are peas, beans, clover, and soy.

Nature of the mutualism Edit

The legume–rhizobium symbiosis is a classic example of mutualism—rhizobia supply ammonia or amino acids to the plant and in return receive organic acids (principally as the dicarboxylic acids malate and succinate) as a carbon and energy source. However, because several unrelated strains infect each individual plant, a classic tragedy of the commons scenario presents itself. Cheater strains may hoard plant resources such as polyhydroxybutyrate for the benefit of their own reproduction without fixing an appreciable amount of nitrogen. [14] Given the costs involved in nodulation and the opportunity for rhizobia to cheat, it may be surprising that this symbiosis should exist at all.

Infection and signal exchange Edit

The formation of the symbiotic relationship involves a signal exchange between both partners that leads to mutual recognition and development of symbiotic structures. The most well understood mechanism for the establishment of this symbiosis is through intracellular infection. Rhizobia are free living in the soil until they are able to sense flavonoids, derivatives of 2-phenyl-1.4-benzopyrone, which are secreted by the roots of their host plant triggering the accumulation of a large population of cells and eventually attachment to root hairs. [15] [16] These flavonoids then promote the DNA binding activity of NodD which belongs to the LysR family of transcriptional regulators and triggers the secretion of nod factors after the bacteria have entered the root hair. [16] Nod factors trigger a series of complex developmental changes inside the root hair, beginning with root hair curling and followed by the formation of the infection thread, a cellulose lined tube that the bacteria use to travel down through the root hair into the root cells. [17] The bacteria then infect several other adjacent root cells. This is followed by continuous cell proliferation resulting in the formation of the root nodule. [15] A second mechanism, used especially by rhizobia which infect aquatic hosts, is called crack entry. In this case, no root hair deformation is observed. Instead the bacteria penetrate between cells, through cracks produced by lateral root emergence. [18]

Inside the nodule, the bacteria differentiate morphologically into bacteroids and fix atmospheric nitrogen into ammonium, using the enzyme nitrogenase. Ammonium is then converted into amino acids like glutamine and asparagine before it is exported to the plant. [15] In return, the plant supplies the bacteria with carbohydrates in the form of organic acids. [15] The plant also provides the bacteroid oxygen for cellular respiration, tightly bound by leghaemoglobins, plant proteins similar to human hemoglobins. This process keeps the nodule oxygen poor in order to prevent the inhibition of nitrogenase activity. [15]

Recently, a Bradyrhizobium strain was discovered to form nodules in Aeschynomene without producing nod factors, suggesting the existence of alternative communication signals other than nod factors, possibly involving the secretion of the plant hormone cytokinin. [15] [19]

It has been observed that root nodules can be formed spontaneously in Medicago without the presence of rhizobia. [20] This implies that the development of the nodule is controlled entirely by the plant and simply triggered by the secretion of nod factors.

Evolutionary hypotheses Edit

The sanctions hypothesis Edit

There are two main hypotheses for the mechanism that maintains legume-rhizobium symbiosis (though both may occur in nature). The sanctions hypothesis theorizes that legumes cannot recognize the more parasitic or less nitrogen fixing rhizobia, and must counter the parasitism by post-infection legume sanctions. In response to underperforming rhizobia, legume hosts can respond by imposing sanctions of varying severity to their nodules. [21] These sanctions include, but are not limited to reduction of nodule growth, early nodule death, decreased carbon supply to nodules, or reduced oxygen supply to nodules that fix less nitrogen. Within a nodule, some of the bacteria differentiate into nitrogen fixing bacteroids, which have been found to be unable to reproduce. [22] Therefore, with the development of a symbiotic relationship, if the host sanctions hypothesis is correct, the host sanctions must act toward whole nodules rather than individual bacteria because individual targeting sanctions would prevent any reproducing rhizobia from proliferating over time. This ability to reinforce a mutual relationship with host sanctions pushes the relationship toward a mutualism rather than a parasitism and is likely a contributing factor to why the symbiosis exists.

However, other studies have found no evidence of plant sanctions. [23]

The partner choice hypothesis Edit

The partner choice hypothesis proposes that the plant uses prenodulation signals from the rhizobia to decide whether to allow nodulation, and chooses only noncheating rhizobia. There is evidence for sanctions in soybean plants, which reduce rhizobium reproduction (perhaps by limiting oxygen supply) in nodules that fix less nitrogen. [24] Likewise, wild lupine plants allocate fewer resources to nodules containing less-beneficial rhizobia, limiting rhizobial reproduction inside. [25] This is consistent with the definition of sanctions, although called "partner choice" by the authors. Some studies support the partner choice hypothesis. [26] While both mechanisms no doubt contribute significantly to maintaining rhizobial cooperation, they do not in themselves fully explain the persistence of the mutualism. The partner choice hypothesis is not exclusive from the host sanctions hypothesis, as it is apparent that both of them are prevalent in the symbiotic relationship. [27]

Evolutionary history Edit

The symbiosis between nitrogen fixing rhizobia and the legume family has emerged and evolved over the past 66 million years. [28] [29] Although evolution tends to swing toward one species taking advantage of another in the form of noncooperation in the selfish-gene model, management of such symbiosis allows for the continuation of cooperation. [30] When the relative fitness of both species is increased, natural selection will favor the symbiosis.

To understand the evolutionary history of this symbiosis, it is helpful to compare the rhizobia-legume symbiosis to a more ancient symbiotic relationship, such as that between endomycorrhizae fungi and land plants, which dates back to almost 460 million years ago. [31]

Endomycorrhizal symbiosis can provide many insights into rhizobia symbiosis because recent genetic studies have suggested that rhizobia co-opted the signaling pathways from the more ancient endomycorrhizal symbiosis. [32] Bacteria secrete Nod factors and endomycorrhizae secrete Myc-LCOs. Upon recognition of the Nod factor/Myc-LCO, the plant proceeds to induce a variety of intracellular responses to prepare for the symbiosis. [33]

It is likely that rhizobia co-opted the features already in place for endomycorrhizal symbiosis, because there are many shared or similar genes involved in the two processes. For example, the plant recognition gene, SYMRK (symbiosis receptor-like kinase) is involved in the perception of both the rhizobial Nod factors as well as the endomycorrhizal Myc-LCOs. [34] The shared similar processes would have greatly facilitated the evolution of rhizobial symbiosis, because not all the symbiotic mechanisms would have needed to develop. Instead the rhizobia simply needed to evolve mechanisms to take advantage of the symbiotic signaling processes already in place from endomycorrhizal symbiosis.

Many other species of bacteria are able to fix nitrogen (diazotrophs), but few are able to associate intimately with plants and colonize specific structures like legume nodules. Bacteria that do associate with plants include the actinobacteria Frankia, which form symbiotic root nodules in actinorhizal plants, although these bacteria have a much broader host range implying the association is less specific than in legumes. [15] Additionally, several cyanobacteria like Nostoc are associated with aquatic ferns, Cycas and Gunneras, although they do not form nodules. [35] [36]

Additionally, loosely associated plant bacteria, termed endophytes, have been reported to fix nitrogen in planta. [37] These bacteria colonize the intercellular spaces of leaves, stems and roots in plants [38] but do not form specialized structures like rhizobia and Frankia. Diazotrophic bacterial endophytes have very broad host ranges, in some cases colonizing both monocots and dicots. [39]

Cell-Surface Receptors

Cell-surface receptors, also known as transmembrane receptors, are cell surface, membrane-anchored (integral) proteins that bind to external ligand molecules. This type of receptor spans the plasma membrane and performs signal transduction, in which an extracellular signal is converted into an intercellular signal. Ligands that interact with cell-surface receptors do not have to enter the cell that they affect. Cell-surface receptors are also called cell-specific proteins or markers because they are specific to individual cell types.

Each cell-surface receptor has three main components: an external ligand-binding domain, a hydrophobic membrane-spanning region, and an intracellular domain inside the cell. The ligand-binding domain is also called the extracellular domain. The size and extent of each of these domains vary widely, depending on the type of receptor.

Because cell-surface receptor proteins are fundamental to normal cell functioning, it should come as no surprise that a malfunction in any one of these proteins could have severe consequences. Errors in the protein structures of certain receptor molecules have been shown to play a role in hypertension (high blood pressure), asthma, heart disease, and cancer.

How Viruses Recognize a Host

Unlike living cells, many viruses do not have a plasma membrane or any of the structures necessary to sustain life. Some viruses are simply composed of an inert protein shell containing DNA or RNA. To reproduce, viruses must invade a living cell, which serves as a host, and then take over the host’s cellular apparatus. But how does a virus recognize its host?

Viruses often bind to cell-surface receptors on the host cell. For example, the virus that causes human influenza (flu) binds specifically to receptors on membranes of cells of the respiratory system. Chemical differences in the cell-surface receptors among hosts mean that a virus that infects a specific species (for example, humans) cannot infect another species (for example, chickens).

However, viruses have very small amounts of DNA or RNA compared to humans, and, as a result, viral reproduction can occur rapidly. Viral reproduction invariably produces errors that can lead to changes in newly produced viruses these changes mean that the viral proteins that interact with cell-surface receptors may evolve in such a way that they can bind to receptors in a new host. Such changes happen randomly and quite often in the reproductive cycle of a virus, but the changes only matter if a virus with new binding properties comes into contact with a suitable host. In the case of influenza, this situation can occur in settings where animals and people are in close contact, such as poultry and swine farms. Once a virus jumps to a new host, it can spread quickly. Scientists watch newly appearing viruses (called emerging viruses) closely in the hope that such monitoring can reduce the likelihood of global viral epidemics.

Cell-surface receptors are involved in most of the signaling in multicellular organisms. There are three general categories of cell-surface receptors: ion channel-linked receptors, G-protein-linked receptors, and enzyme-linked receptors.

Figure 2. Gated ion channels form a pore through the plasma membrane that opens when the signaling molecule binds. The open pore then allows ions to flow into or out of the cell.

Ion channel-linked receptors bind a ligand and open a channel through the membrane that allows specific ions to pass through. To form a channel, this type of cell-surface receptor has an extensive membrane-spanning region. In order to interact with the phospholipid fatty acid tails that form the center of the plasma membrane, many of the amino acids in the membrane-spanning region are hydrophobic in nature. Conversely, the amino acids that line the inside of the channel are hydrophilic to allow for the passage of water or ions. When a ligand binds to the extracellular region of the channel, there is a conformational change in the proteins structure that allows ions such as sodium, calcium, magnesium, and hydrogen to pass through (Figure 2).

G-protein-linked receptors bind a ligand and activate a membrane protein called a G-protein. The activated G-protein then interacts with either an ion channel or an enzyme in the membrane (Figure 3). All G-protein-linked receptors have seven transmembrane domains, but each receptor has its own specific extracellular domain and G-protein-binding site.

Cell signaling using G-protein-linked receptors occurs as a cyclic series of events. Before the ligand binds, the inactive G-protein can bind to a newly revealed site on the receptor specific for its binding. Once the G-protein binds to the receptor, the resultant shape change activates the G-protein, which releases GDP and picks up GTP. The subunits of the G-protein then split into the α subunit and the βγ subunit. One or both of these G-protein fragments may be able to activate other proteins as a result. After awhile, the GTP on the active α subunit of the G-protein is hydrolyzed to GDP and the βγ subunit is deactivated. The subunits reassociate to form the inactive G-protein and the cycle begins anew.

Figure 3. Heterotrimeric G proteins have three subunits: α, β, and γ. When a signaling molecule binds to a G-protein-coupled receptor in the plasma membrane, a GDP molecule associated with the α subunit is exchanged for GTP. The β and γ subunits dissociate from the α subunit, and a cellular response is triggered either by the α subunit or the dissociated βγ pair. Hydrolysis of GTP to GDP terminates the signal.

G-protein-linked receptors have been extensively studied and much has been learned about their roles in maintaining health. Bacteria that are pathogenic to humans can release poisons that interrupt specific G-protein-linked receptor function, leading to illnesses such as pertussis, botulism, and cholera.

Figure 4. Transmitted primarily through contaminated drinking water, cholera is a major cause of death in the developing world and in areas where natural disasters interrupt the availability of clean water. (credit: New York City Sanitary Commission)

In cholera (Figure 4), for example, the water-borne bacterium Vibrio cholerae produces a toxin, choleragen, that binds to cells lining the small intestine. The toxin then enters these intestinal cells, where it modifies a G-protein that controls the opening of a chloride channel and causes it to remain continuously active, resulting in large losses of fluids from the body and potentially fatal dehydration as a result.

Modern sanitation eliminates the threat of cholera outbreaks, such as the one that swept through New York City in 1866. This poster from that era shows how, at that time, the way that the disease was transmitted was not understood.

Enzyme-linked receptors are cell-surface receptors with intracellular domains that are associated with an enzyme. In some cases, the intracellular domain of the receptor itself is an enzyme. Other enzyme-linked receptors have a small intracellular domain that interacts directly with an enzyme. The enzyme-linked receptors normally have large extracellular and intracellular domains, but the membrane-spanning region consists of a single alpha-helical region of the peptide strand. When a ligand binds to the extracellular domain, a signal is transferred through the membrane, activating the enzyme. Activation of the enzyme sets off a chain of events within the cell that eventually leads to a response. One example of this type of enzyme-linked receptor is the tyrosine kinase receptor (Figure 5). A kinase is an enzyme that transfers phosphate groups from ATP to another protein. The tyrosine kinase receptor transfers phosphate groups to tyrosine molecules (tyrosine residues). First, signaling molecules bind to the extracellular domain of two nearby tyrosine kinase receptors. The two neighboring receptors then bond together, or dimerize. Phosphates are then added to tyrosine residues on the intracellular domain of the receptors (phosphorylation). The phosphorylated residues can then transmit the signal to the next messenger within the cytoplasm.

Practice Question

Figure 5. A receptor tyrosine kinase is an enzyme-linked receptor with a single transmembrane region, and extracellular and intracellular domains. Binding of a signaling molecule to the extracellular domain causes the receptor to dimerize. Tyrosine residues on the intracellular domain are then autophosphorylated, triggering a downstream cellular response. The signal is terminated by a phosphatase that removes the phosphates from the phosphotyrosine residues.

HER2 is a receptor tyrosine kinase. In 30 percent of human breast cancers, HER2 is permanently activated, resulting in unregulated cell division. Lapatinib, a drug used to treat breast cancer, inhibits HER2 receptor tyrosine kinase autophosphorylation (the process by which the receptor adds phosphates onto itself), thus reducing tumor growth by 50 percent. Besides autophosphorylation, which of the following steps would be inhibited by Lapatinib?

Scientists Make Bacteria Behave Like Computers

Bacteria have been programmed to behave like computers, assembling themselves into complex shapes based on instructions stuffed into their genes.

The research could lead to smart biological devices that could detect hazardous substances or bioterrorism chemicals, scientists say. Eventually, the process might be used to direct the construction of useful devices or the growth of new tissue, perhaps restoring function to a severed spinal cord.

Many lines of research hold similar promise for controlling biology to build useful things. Predictions do not always come true. What's new about this latest effort is that the bacteria are made to communicate, so that millions or even billions of them gather in a predictable manner.

And there are pictures to prove it.

The researchers programmed E. coli bacteria to emit red or green fluorescent light in response to a signal emitted from another set of E. coli. The living cells were commanded to make a bull's-eye pattern, for example, around central cells based on communication between the bacteria.

Other patterns produced with this new "synthetic biology" technique include a pretty good semblance of a heart and a rudimentary flower pattern.

The work was led by Ron Weiss, an assistant professor of electrical engineering and molecular biology at Princeton University.

Weiss and his colleagues engineer a special segment of DNA, the blueprints for any cell's operations. The segment is called a plasmid.

"You have a segment of DNA that dictates when proteins should be made and under what conditions," Weiss told LiveScience. The plasmid is inserted into a cell, and "the cell then executes the set of instructions."

While most real-world applications of the technique are likely many years away, Weiss said it might be used in three to five years to make devices that could detect bioterrorism chemicals.

The bacteria "have an exquisite capability to sense molecules in the environment," he said. "The bull's-eye could tell you: This is where the anthrax is."

The study is detailed in April 28 issue of the journal Nature.

In a paper March 8 in the Proceedings of the National Academy of Sciences, another team led by Weiss showed they could insert DNA into cells to make them behave like digital circuits. The cells could be made to perform basic mathematical logic. The latest work expands this concept to vast numbers of bacteria responding in concert.

"Here we're showing an integrated package where the cells have an ability to send messages and other cells have the ability to act on these messages," Weiss said.

'Greenish signals'

Under a microscope, bacteria in the cracks lit up as glowing green spheres, visible in long, twisting tunnels inside the rocks chemical analysis confirmed that the "greenish signals" came from microbial DNA and not from fluorescent structures in the minerals. Surrounding the bacteria was fine-grained clay rich in organic carbon, providing vital nutrients for the colonies, according to the study.

Further genetic analysis revealed that there were different bacteria species colonizing rocks that were different ages, perhaps because variations in heat and water flow over millions of years shaped the accumulation of different minerals that fed the bacteria, the researchers reported.

Finding densely packed and thriving microbial communities in this unlikely environment also offers hope for locating microbes that could survive in similar rocky homes on other worlds, such as Mars, Suzuki said in the statement.

"This discovery of life where no one expected it in solid rock below the seafloor may be changing the game for the search for life in space," Suzuki said.

The findings were published online April 2 in the journal Communications Biology.

Bacteria Could Grow Futuristic 'Self-Healing' Materials

Why bother to manufacture materials if you can grow them organically?

Researchers have produced "living" materials by nudging bacteria to grow biological films. In turn, this process could lead to the development of more complex and interactive structures programmed to self-assemble into specific patterns, such as those used on solar cells and diagnostic sensors, and even self-healing materials that could sense damage and repair it, a new study finds.

"In contrast to materials we use in modern life, which are all dead, living materials have the ability to self-heal, adapt to the environment, form into complex patterns and shapes, and generate new functional materials and devices from the bottom up," said study lead author Timothy Lu, a biological engineer at the Massachusetts Institute of Technology.

Such "living materials" are essentially hybrids that have the best of both worlds: the benefits of both living cells, which can organize and grow on their own, and nonliving materials, which add functions such as electricity conduction or light emission. [Biomimicry: 7 Clever Technologies Inspired by Nature]

For instance, other researchers have looked at the possibility of organizing viruses into new materials. But Lu said his team's approach is different. "Previous systems do not leverage the characteristics of living organisms," he told Live Science. "Also, most modern materials' synthesis processes are energy-intensive, human-intensive endeavors. But we're suggesting to use biology to grow materials from the bottom up in an environmentally friendly fashion."

Learning from bones

To create the materials, Lu's team took inspiration from natural materials, such as bone and teeth, which contain a mix of minerals and living cells. Bones grow when cells arrange themselves into specific patterns and then excrete special proteins to produce the calcium phosphate structures.

Lu's team tried to do the same by reprogramming Escherichia colibacterial cells using genetic engineering to produce the proteins.

E. colinaturally produce biofilms that contain a special type of protein called curli fibers that help the bacteria attach to surfaces, and are known to have the strength of steel. Each curli fiber is composed of a chain of identical protein units called CsgA, which can be changed by adding protein fragments called peptides. These peptides can capture nonliving materials, such as gold nanoparticles, and incorporate them into the biofilms.

The researchers' goal was to get the bacteria to secrete the protein matrix in response to specific stimulants.

To do so, the researchers disabled the bacterial cells' natural ability to produce CsgA and replaced it with an engineered genetic code that produces CsgA proteins only under certain conditions &mdash when a molecule called AHL is present.

The scientists could then adjust the amount of AHL in the cells' environment, and when AHL was present, the cells produced CsgA, making curli fibers that merged into a biofilm.

The team then modified E. coli in a different way, to make it produce CsgA with a specific peptide with many histidine amino acids, but only when a molecule called aTc was present.

"This allowed us to control the materials that were made by the bacteria using external signals," said Lu. Just by increasing or decreasing the amount of AHL and aTc in the modified E. coli's environment, they were able to modify the production and composition of the resulting biofilms.

The team then modified the proteins to make inorganic materials, such as gold nanoparticlesand quantum dots, to grow on the biofilms. By doing so, the researchers engineered self-growing E. coli biofilms that could conduct electricity or emit fluorescence.

"Talking" cells

The researchers also modified E. coli so the cells could "talk" to each other and coordinate the formation of materials whose properties change over time, without requiring human input. "Ultimately, we hope to emulate how natural systems, like bone, form. No one tells bone what to do, but it generates a material in response to environmental signals," Lu said. [Bone Basics: 11 Surprising Facts About the Skeletal System]

"One can imagine growing materials using sunlight rather than needing to have very energy-intensive processes for top-down materials' synthesis," he added.

Lu also envisions living cellular sensors that change their properties when they detect specific environmental signals, such as toxins.

Finally, by coating the biofilms with enzymes that catalyze the breakdown of cellulose, this work could lead to materials that convert agricultural waste into biofuels.

The research is not limited to E. coli. "We are considering the use of photosynthetic organisms and fungi as other fabrication platforms," Lu said. "In addition, we have only demonstrated the interface of biology with gold and semiconductor nanocrystals, but there are many other materials that can be interfaced."

Ahmad Khalil, a biomedical engineer at Boston University who was not involved in the study, applauded the work.

"This work presents, to my knowledge, one of the first demonstrations of using synthetic biology approaches to rewire or engineer these cellular mechanisms to precisely control how inorganic materials are assembled or synthesized on a molecular bio-template, thus providing an avenue for genetically encoded materials engineering," Khalil told Live Science.

The study was detailed in the March 23 issue of the journal Nature Materials.



The contents of the publicly accessible Neisseria MLST database [17, 18] were used to explore the validity of the approach described here for other species. Alleles at the seven MLST loci of all isolates defined as Neisserial species other than N. meningitidis (67 isolates of N. gonorrhoeae, 171 of N. lactamica, 5 of N. sicca, 3 of N. mucosa, 5 of N. cinerea, 7 of N. polysacchareae, 3 of N. flava, 4 of N. perflava, 4 of N. subflava and 1 isolate of N. flavescens) were concatenated as described below, and analysed together with the concatenated sequences of N. meningitidis strains with ST numbers from 1 to 500. Species definitions were as recorded at [17, 18], and were according to standard clinical microbiological schema. The sequences of the individual alleles at the seven loci in the above Neisseria were also used to construct individual gene trees.

Phylogenetics and population genetics

MLST loci were concatenated in-frame to form a 3267 bp sequence, of which only third position sites were used in subsequent analyses. To illustrate clustering in this dataset, a tree was constructed using Mr Bayes 3.0b4 [19]. A starting tree was determined in PAUP (version 4 beta 10) [29] using the Neighbour-Joining method with distances corrected using the HKY85 model. The starting tree was input into Mr Bayes, and four Markov Chain Monte Carlo chains were run with default heating parameters until convergence and 10 000 trees were sampled from the posterior probability distribution. These were then used to produce a 50% majority rule consensus tree. Minimum evolution trees for individual loci were constructed in MEGA 2.1 [30]. Third position sites were used with the Kimura 2-parameter distance correction.

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