Custom-built microscopes often require control of multiple hardware devices and precise hardware coordination. It is also desirable to have a solution that is scalable to complex systems and that is translatable between components from different manufacturers. Here we report Python-Microscope, a free and open-source Python library for high-performance control of arbitrarily complex and scalable custom microscope systems. Python-Microscope offers simple to use Python-based tools, abstracting differences between physical devices by providing a defined interface for different device types. Concrete implementations are provided for a range of specific hardware, and a framework exists for further expansion. Python-Microscope supports the distribution of devices over multiple computers while maintaining synchronisation via highly precise hardware triggers. We discuss the architectural features of Python-Microscope that overcome the performance problems often raised against Python and demonstrate the different use cases that drove its design: integration with user-facing projects, namely the Microscope-Cockpit project; control of complex microscopes at high speed while using the Python programming language; and use as a microscope simulation tool for software development. Summary: An open-source Python library for accessing a range of hardware devices used in microscopy. The most advanced methods and applications in modern microscopy often require the assembly of custom-built specialised microscope systems. Such one-off systems may range from an off-the-shelf microscope stand with a range of specialised devices attached, to an advanced engineering project built from custom optical components. There are many unique examples in the literature of such systems. When building most such systems, software development to control a specific combination of hardware devices forms a large portion of the effort. Other than developing software from scratch, there are currently three popular routes for controlling custom microscopes: individual device vendor software unique to each manufacturer; microscope-specialised control software, with the most popular being Micro-Manager (μManager), a general Java-based framework with a graphical user interface (GUI) that has the lowest barrier to use; and custom control software, often written in LabVIEW or MATLAB, both of which are proprietary software. LabVIEW offers a visual programming environment that is commonly used for building instruments in the physical sciences, whereas MATLAB is a programming environment with a focus on numeric computing.The solution requiring the least specialist knowledge is to use the device vendor software for the individual microscope components and run them in parallel. For example, one would open the light source, the camera and the stage programs separately, and place their individual windows side by side on the monitor to easily access all the controls. This solution is very convenient for simpler microscopes but lacks flexibility and scalability. Because the individual devices are being controlled using separate programs that do not communicate with each other, control of the microscope is effectively manual. This limits microscope usage to experiments that do not require synchronisation between devices. It also becomes extremely unwieldy; as more devices are used, each inevitably requires its own separate program and window. Finally, with very rare exceptions, although these programs are often distributed at no cost, they are not open-source software.μManager is a general free and open-source program for control of microscopes . It is whttps://www.ni.com/en-us/shop/labview.html) is a visual programming language and development environment that is widely used in physical sciences for prototyping instruments. It has very wide hardware support, with many manufacturers providing LabVIEW modules  to control their devices. Moreover, it allows very rapid development of GUIs with no previous programming knowledge. LabVIEW is proprietary and requires a commercial license for every computer it is used on, likely with associated cost. As each LabVIEW module is written by the equipment manufacturer, there is little consistency in how to interface different hardware. The visual nature of the programming environment makes simple projects easy, but systems with a large number of hardware components or complicated control architecture can become hard to understand, reproduce and maintain. Although this complication can be reduced with good programming practices, it is not uncommon to outsource such work to a commercial company  for single-molecule localisation microscopy (SMLM) and ACQ4  code that is independent of specific device models and vendors; (2) creation of Python programs as microscope experiments; (3) distribution of devices over the network and horizontal scaling; (4) integration of high-performance hardware triggers and software triggers; (5) support of both software and hardware developers during the development phase. Here we describe these use cases and how Python-Microscope addresses them, with the details of implementation being described later.A major design consideration is the ability to reuse code upon modification or reimplementation of a given microscope. This happens frequently: when replacing a specific device with a different model, either because the device failed or to get access features such as faster acquisition rates; when building a copy of the microscope on another site that has access to different devices; or as part of a larger improvement to an existing system, such as addition of a second light path with another camera. The ability to reuse the code after such changes to the system is critical, because it accelerates microscope development by eliminating or reducing the software development work, which is incidental to the main purpose of the microscope.At its core, Python-Microscope is a Python package that provides a defined interface for the different device types that may be used on a microscope. This means that code written for a specific device type, such as a camera or light source, will work for all devices of that type. For example, to control the power output of a light source, manufacturer-supplied control software for different devices not only uses different commands and names – such as power, intensity or flux – but also different values – such as percentage, 0–1 range or intensity in milliwatts. In Python-Microscope, the interface for light sources specifies the ‘power’ attribute with a value in the 0–1 range for all light sources no matter what the underlying device implements.Fig. S1) and PYME (https://www.python-microscopy.org/), programs that provide a GUI for complete microscopes and support a wide range of imaging modalities and a number of devices.A simple example of Python-Microscope being used in this way is the BeamDelta program  for aligThe ability to write programs that are effectively microscopy experiments was an important goal. There are three reasons for this: first, to provide as much flexibility as possible, since GUIs are inherently limited by specific predesigned experiment types; second, to enable feedback of arbitrary image analysis into any step of the experiment control; and third, to simplify the sharing of even extremely complex experiments. The ability to write experiments as programs provides all these features.https://github.com/python-microscope/). Python also provides an interactive environment, making development and debugging of systems as well as prototyping of experiments easily accessible.As Python-Microscope is a software library for controlling individual devices, it automatically supports the ability to create programs that are experiments. We have developed Python-Microscope so that such programs are easy to read and write, and even complex experiments can be encoded in easy-to-understand code  and reduces performance. In addition to the delay caused by the communication itself, there is unpredictable delay (jitter) introduced by the operating system context switching and asynchronous communication protocols, such as networks and USB. In total, these unpredictable timing issues can introduce changes of tens of milliseconds, higher than some experimental camera exposure times, making software control impossible for such demanding experiments.Many devices can be configured to act on receipt of an electrical signal, often a transistor–transistor logic (TTL) signal voltage, referred to as a hardware trigger. This hardware-based control is deterministic, as it does not depend on any software. For instance, a laser might only emit light while receiving a high TTL input signal, a camera can start an image acquisition after receipt of a TTL rising edge signal, or a TTL signal might prompt a deformable mirror to apply the next queued pattern. A device may also generate a trigger signal. For instance, cameras and light sources can often be configured to output TTL signals while exposing or emitting light, respectively.The Python-Microscope interface was designed with the concept of triggers that activate the individual devices, and software triggers are handled as simply another trigger type. This approach provides an interface that supports software triggers but is easily upgraded to support hardware triggers. The source of such hardware triggers can be other devices – typically a camera – or a dedicated triggering device. The recommended procedure is to prepare an experiment template that is then loaded on a dedicated timing device that triggers all other devices, as described in The existence of fast and cheap microprocessors and single board computers mean that providing a dedicated hardware timing to sequence and synchronise a number of devices is relatively easy and extremely cost effective. We recommend that systems are designed around using an external device to provide hardware triggers to devices. This provides reliable timing and much more flexible sequencing than directly connecting outputs from one device to trigger inputs for another.Finally, Python-Microscope is a useful tool during the system development phase. There are two sides of system development: the development of the actual optical setup and the development of the software to control that optical setup.z axis of the simulated stage and a ‘channel’ is selected based on the position of the simulated filter wheel. With hardware and software development happening concurrently, it is practical for one person to build the optical system and connect hardware while another is building the software. In doing so, total build time and costs can be reduced.To support development of optical systems, Python-Microscope includes a simple GUI program for the different device types . These ahttps://python-microscope.org. Here, we include an overview of the design and implementation details as well as suggested workflows for hardware control using Python-Microscope.Documentation for the software and usage examples are available online at abc standard library. These device ABCs are incomplete implementations with concrete methods, which handle input validation and provide software fallbacks, and abstract methods for the individual device operations.Python-Microscope interfaces for the different device types are implemented as abstract base classes (ABCs) using Python's Device ABC, which defines core operations, such as device settings and shutdown. The interface to support hardware triggers is implemented via an ABC mixin, named TriggerTargetMixin, which is mixed with the individual device type ABCs.Currently, ABCs have been implemented for cameras, light sources, filter wheels, stages and deformable mirrors, as well as integrated controllers that can control multiple pieces of hardware. All these ABCs inherit from a ctypes standard library which removes the need for a compilation step or dependence on other tools. Direct communication is typically performed via RS-232 serial connections using Python's pySerial package , frequently over USB to serial bridges in connected hardware.The implementation of a concrete class  is device dependent, but typically it either wraps a vendor-provided C library or a direct communication link to the hardware. In the case of a C library, these are dynamically loaded with Python's https://python-microscope.org).Concrete implementations have been created for multiple devices. The full list is expanding with each new release and is part of the Python-Microscope documentation available online (device-server program creates a Pyro4 daemon (https://pyro4.readthedocs.io/) for each device. The Python-Microscope device instance is created and then registered with its Pyro4 daemon. There is only one Python-Microscope device instance per physical device. The Python multiprocessing standard library is used to place each Pyro4 daemon in its own Python process, effectively sidestepping Python's global interpreter lock (GIL). The device server can be configured to serve a set of devices on the same process in order to simplify inter-device communication, typically for composite virtual devices. Other instances that provide control of the hardware, such as individual stage axis and devices behind a controller, are registered with the Pyro4 daemon at initialisation. With Pyro4's autoproxy feature, these other instances are automatically replaced by a Pyro4 proxy on the client side.The While the use of device server is recommended, its implementation is decoupled from the device classes themselves and its use is entirely optional. This allows simple actions like debugging or testing partial systems to be performed directly via an interactive Python shell.microscope-gui program makes use of the QtPy package (https://pypi.org/project/QtPy/), which abstracts the difference between PyQt5, PyQt4, PySide2 and PySide, which are all different Python wrappers to Qt (https://www.qt.io/), thus making the dependency a choice of the user. The dependency on QtPy itself is optional, making Python-Microscope easier to install if the GUI is not required.The microscope-gui program window. The widget classes are public, so they can be incorporated in other programs. It should be noted that microscope-gui is not intended as a generalised user interface to control microscopes, we expect it to be used for low-level hardware control under other software like Microscope-Cockpit .For each device ABC there is a corresponding QWidget class. This widget is the central and only widget of the binning property but instances of PVCamera might also have the BINNING_SER and BINNING_PAR settings. Direct use of such settings, instead of the defined interface, ties the resulting code to the specific device, reducing potential for reuse; hence, we recommend against such practice if possible.An inherent limitation of defining an interface for different device types is the use of specialised device features that are specific to single device model or range of models. For example, some cameras have the option of applying denoising filters during acquisition, or provide control over amplifiers in the different stages of the camera readout. These features are exposed via a general ‘settings’ mode, which is effectively a map of feature names to values. Python-Microscope provides such settings even when they clash with properties that are part of Python-Microscope's interface. For example, Python-Microscope's camera interface has a https://buildmedia.readthedocs.org/media/pdf/pillow/latest/pillow.pdf), Pyro4, hidapi (https://pypi.org/project/hidapi/), and pySerial . These are all free software and widely available for all operating systems, ensuring that Python-Microscope is truly cross platform. We have run Python-Microscope on different versions of Microsoft Windows, different GNU/Linux distributions and macOS, on a multitude of computers from high-end workstation computers and rack servers, to touchscreen notebooks and single-board Raspberry Pi computers.Python-Microscope has simple computational requirements. It requires Python 3.6 or later, and the Python packages NumPy , Pillow device-server, a controlling client program can be on another computer with a different operating system from the device-server.Despite the simple requirements of Python-Microscope itself, individual devices often have their own more demanding requirements. Some devices require specific hardware connectors such as PCIe slots of a specific mode or size, which can limit the motherboard or computer chassis used – for example, laptops and all-in-one computers very rarely contain any PCIe slots, which are required for high-speed and high-resolution cameras. Other devices may require a serial port or USB of a specific version. Similarly, use of devices at high speed will add further system requirements – for example, acquiring many images at a fast rate will require faster data transfer and more computer memory. Finally, some of the device software development kits (SDKs) that Python-Microscope wraps are only available for one operating system, usually specific versions of Microsoft Windows. Note though that by using a network connection to the device-server program to create the devices and have programs control the devices via Pyro4 proxies. This removes the need for device specific code on the program, replacing it with configuration files that only need the device type and Pyro4 universal resource identifier (URI), both of which are strings that can be encoded on a plain text file. In the specific case of camera devices, the program needs to register the data client with a Pyro4 daemon and pass its URI to the camera device instance. This enables asynchronous transfer of images back to the controlling process.Python-Microscope provides an application programming interface (API) to microscope devices of different types. Their documentation is included as part of the class docstrings. Although any program can easily instantiate the class of their specific hardware, our recommendation is to use the microscope-gui works as a simple example of the use of Python-Microscope in a program for microscope control. The program is called with two arguments, the device type and its URI, a simpler form of configuration. Its CameraWidget class is also a minimal example of instantiating a queue object and serving it on its own dedicated Pyro4 daemon to be used as data client for the camera device.The Python-Microscope is a library to enable builders of custom microscope systems to fully exploit the power of the increasingly popular Python programming language. It interfaces with a growing range of hardware classes, including cameras, filter wheels, light sources, deformable mirrors and stages with defined APIs, while still allowing access to device-specific features. Python-Microscope provides simple access to complex devices for Python programs and a defined protocol for accessing devices either locally or over network connections via the device server. The device server deals transparently with hardware restarts through reinitialisation and other issues, providing a reliable and robust interface to hardware in complex systems and environments.Python is a free, modern, open-source, portable programming language. It has wide support and a large developer community, is relatively easy to learn and has a wide ecosystem for scientific computing. However, Python is not without its drawbacks. In the context of hardware control, namely the coordination of multiple hardware devices, the most commonly mentioned issue is Python's performance and lack of ‘true’ multithreading due to a GIL.As mentioned in the Introduction, μManager has recently introduced a Python interface, Pycro-Manager . This siPython has a reputation for being slow when compared to C, C++ or even Java, apparently making it an odd choice for microscope control where multiple physical devices have to be controlled at high speed in an orchestrated manner. However, multithreaded operating systems simply do not have reliable enough timing for the triggering of critical components, whichever programming language is used. For these applications, we recommend the use of hardware trigger modes that many devices have (or that are available as options). This approach was one of the main design considerations for Python-Microscope, as it makes Python's performance mostly irrelevant to the overall performance of the microscope system. Moreover, we recommend the use of Python-Microscope in a client–server model to access the different devices, each of them with a dedicated Python process, thus side stepping the GIL.ctypes library, which also releases the GIL.Even if hardware triggers and a client–server model are not used, our experience is that Python's reputation as being slow is unjust. At least in the case of scientific computing, the core processing steps in Python are often implemented in C or Fortran. In Python-Microscope, most of the hardware controllers are simply wrappers to vendor-provided libraries that are written in C. In addition, calls to functions in those C libraries are done with Python's http://www.scipy.org), and TensorFlow (https://www.tensorflow.org/) are well known examples of Python programs and libraries. Novel advances in the field of machine learning are increasingly happening first in the Python programming language. By using the Python language, Python-Microscope can harness all of these and bring new developments into the field of microscope control more quickly and easily, accelerating the advancement of the field to what we see as the next logic step , and the project is an image.sc community partner (https://forum.image.sc/). In particular, expanding the number of devices supported by Python-Microscope would be extremely beneficial. However, adding support for a device requires physical access to the device, and the current list of supported devices echoes the devices we and our collaborators have access to. This is a chicken and egg problem. Python-Microscope needs broad device support to be widely adopted by the community, but it needs contributions from the community to support those devices. We believe that Python-Microscope currently provides enough devices and infrastructure to support adoption by more developers. There are contribution guidelines within the ‘Get Involved’ section of the documentation, available online at https://www.python-microscope.org/doc/get-involved. In an effort to minimise duplication and increase community cooperation, we are keen to foster links and collaborations with other Python-based or Python-compatible microscope control packages, such as PYME and μManager. We will endeavour to make bindings, when possible, that will enable interoperability of Python-Microscope with other open community-based Python software controlling microscope hardware.Python-Microscope is a free and open-source project, currently being used in several labs, with an open development approach. Our aim is that the microscope development community will find Python-Microscope a useful tool and engage in this development to increase its general usefulness. With that aim in mind, we perform our development conversations and user support in the open, as GitHub issues . Development sources are available from GitHub (https://github.com/python-microscope/microscope). Documentation is included in the source releases as well as HTML on the project website (https://python-microscope.org/).Python-Microscope is distributed under the GNU General Public License version 3.0 or later. Source releases are available for download from the Python Package Index (


Erythrinus erythrinus. We hypothesized that the presence of multiple sex chromosomes has contributed to the genetic differentiation of populations, which could have potentially accelerated speciation.Fish present astonishing diversity, comprising more species than the combined total of all other vertebrates. Here, we integrated cytogenetic and genomic data to investigate how the evolution of multiple sex chromosomes together with allopatry is linked to genetic diversity and speciation in the fish species Erythrinus erythrinus  being a notable example. This nominal species shows remarkable intra-specific variation, with extensive karyotype diversity found among populations in terms of different diploid chromosome numbers (2n), karyotype compositions and sex chromosome systems. Here, we analyzed three distinct populations  that differed in terms of their chromosomal features (termed karyomorphs) and by the presence or absence of heteromorphic sex chromosomes. We combined cytogenetics with genomic approaches to investigate how the evolution of multiple sex chromosomes together with allopatry is linked to genetic diversity and speciation. The results indicated the presence of high genetic differentiation among populations both from cytogenetic and genomic aspects, with long-distance allopatry potentially being the main agent of genetic divergence. One population showed a neo-X1X2Y sexual chromosome system and we hypothesize that this system is associated with enhanced inter-population genetic differentiation which could have potentially accelerated speciation compared to the effect of allopatry alone.Diversity found in Neotropical freshwater fish is remarkable. It can even hinder a proper delimitation of many species, with the wolf fish  Hoplias (13), Hoplerythrinus (3), and Erythrinus (2) [The biodiversity of Neotropical freshwater fish is remarkable, as they comprise at least 5200 species . The EryHoplias 1, Hopleryinus (2) , as all E. erythrinus and H. malabaricus, in which many studies have shown the presence of intraspecific variants, with extensive differences observed in the diploid chromosome numbers (2n), karyotype compositions and different sex chromosome systems among populations [E. erythrinus  with a prevalence of acrocentric chromosomes which distinguish the species karyotypes from those of other erythrinids [n = 54 chromosomes in both males and females, without the presence of differentiated sex chromosomes, karyomorphs B, C and D display a multiple ♀X1X1X2X2/♂X1X2Y sex chromosome system, in which males and females have 2n = 51 and 2n = 52, respectively [Such cryptic diversity is at least partially explained by the high karyotypic diversity found in some species, such as ulations . Four dithrinids ,6. Whileectively ,7.Even though allopatry is generally considered the main factor for the emergence of reproductive isolation and the reduction of gene flow that culminates in the speciation process , the impE. erythrinus  and used genomic approaches to investigate how the evolution of multiple sex chromosomes together with allopatry is linked to genetic diversity and speciation. We hypothesize that the presence of multiple sex chromosomes has differentially contributed to the genetic differentiation of populations, which could have potentially accelerated speciation.Recently, high-throughput sequencing technologies have opened a new era in population genetics, as they allow the assessment of complex demographic scenarios, including how gene flow and introgression affect genetic diversity and speciation . In thisThe collection sites, number and sex of the specimens investigated are presented in Here we present the first chromosomal data for population A2. Mitotic chromosomes were obtained by the protocol previously described . The 5S 0t-1 DNA ratio based on previous studies [0t-1 DNA, obtained from each population according to a previous study [0t-1 DNA from the respective population were co-precipitated in 96% ethanol, and the dry pellets were resuspended in 20 µL of hybridization buffer containing 50% formamide, 2 × SSC, 10% dextran sulphate and Denhardt’s buffer (pH 7.0). FISH was conducted under high stringency conditions, as described in [Comparative genomic hybridization (CGH) experiments were performed as described  with ada studies ,29,30. M studies  and labeus study , was incribed in .To confirm the diploid number, karyotype structure and hybridization results, at least 30 metaphase spreads were analyzed per individual. The microscopy images were captured using an Olympus BX50 epifluorescence microscope  coupled with a CoolSNAP camera, and the images were processed using Image-Pro Plus 4.1 Software . Final images were optimized and arranged using Adobe Photoshop, version CC 2020. Chromosomes were classified as metacentric (m), submetacentric (sm), subtelocentric (st) or acrocentric (a), according to their arm ratios . As the The DArTseq sequencing procedures were performed using liver tissues that were shipped to Diversity Arrays Technology Pty Ltd. . This method involves a complexity reduction in which the DNA is treated with PstI and SbfI enzymes before sequencing, which might enrich for sequences in lightly methylated regions . SequencA BayeScan analysis was carried out to test for markers with uncommon levels of inter-population differentiation (low or high), which might indicate locally adaptive alleles at the sequenced loci . We perfdH), Tajima’s D (D) and two measures of nucleotide diversity per site, π and Watterson’s theta (Wθ) averaged per phased loci from dataset 1. We also estimated the nucleotide divergence between pairs of samples from different sites using the average number of nucleotide substitutions per site (XYD) and the net divergence, corrected for the variation within analyzed samples (AD).Genetic diversity indexes were obtained with the software DnaSP v. 6.12.03 . We calcE. erythrinus, as measured by their impact on the model’s covariance. Therefore, when a given K value did not contribute anymore to the result, the previous K was selected [We assessed population structure with a principal coordinate analysis (PCoA) to visualize genetic diversity distribution in each karyomorph. Then, we implemented the fastStructure Bayesian approach . This meselected .E. erythrinus populations, i.e., many acrocentric and few bi-armed chromosomes. The karyotype presented 2n = 54 (6m + 2st + 46a), without differentiated sex chromosomes. In addition, up to three supernumerary B chromosomes were observed, which usually appeared like double-minute chromosomes and stained less than the standard chromosomes  and the subtelocentric pair 4, in addition to one chromosome pair with centromeric sites (pair 14). In turn, the only subtelocentric pair was observed with 5S rDNA sites on the telomeric region of the short arms. This same chromosome pair also bears 18S rDNA sites on its long arms, thus evidencing an unusual syntenic condition concerning these two classes of ribosomal DNAs .The set of our CGH experiments aimed to compare genomes among the different studied populations. The intra-karyomorph comparative hybridization of gDNA probes from A1 and A2 individuals produced essentially many overlapping signals, which highlights their genomic similarities  and suggests significant sequence homology, with some of them probably related to major rDNA sites. Some population-specific signals observed indicate some over-represented signals, probably linked to a higher copy number of repetitive sequences in some chromosomal regions a–d. On tSequencing generated around 2 million reads for each sample. After trimming and filtering steps, we obtained 14,467 phased loci with a minimum coverage of six present in all individuals, which we refer hereafter as dataset 1. Then, we created dataset 2 by maintaining only one biallelic SNP per sequence locus to avoid tightly linked SNPs, which resulted in 8597 polymorphic SNPs. Bayescan did not consider any locus as potentially under selection, therefore no marker was removed from subsequent analysis.The PCoA indicates a clear separation between karyomorphs A and D. Karyomorph D formed two minor groups that are closely related, and karyomorphs A1 and A2 formed one major cluster each, with some individuals scattered between these two clusters A.The non-geographic Bayesian approach fastStructure was congruent with the PcoA result and pointed out K = 2 as the value that maximizes likelihood and best explains the data. The analysis clustered A1 and A2 as a single population and karyomorph D as a second population B. The spWθ) were higher in the A1 locality, while the lowest values were present in D. All sampled groups presented negative Tajima’s D values, with the lowest in A1, followed by A2 and then D. Values of genetic differentiation, XYD and AD, were approximately ten times lower within karyomorph A when values between karyomorphs A and D were contrasted  and Watterson theta , conomosomes . The karomosomes .1 and X2 chromosomes and in the neo-Y chromosome [E. erythrinus, since both karyomorphs A1 and D reveal the same hybridization results for this probe. However, karyomorph A2 (herein described) presents six chromosomes mapped with the 18S rDNA probe . Differences in the distribution of the 18S rDNA among populations have already been described for fish of the same family, as in the case of Hoplias malabaricus . An invreviewed .E. erythrinus karyomorphs. Among fish, species with conserved karyotype macrostructures are also reported to have a variable pattern of genomic organization, as observed for Lebiasina [E. erythrinus that presents a differentiated sex chromosome (karyomorph D). This is in accordance with previous findings that the emergence of a new sex-linked locus or the translocation of an existing one is one of the fundamental steps in fish sex chromosome differentiation [The set of our CGH experiments aimed to compare genomes among the different studied populations. Such comparisons have been found to be very effective in identifying hidden biodiversity in fish (reviewed in ) and in ebiasina , for theebiasina  and for ebiasina . Differeebiasina , trahiraebiasina , pencilfebiasina  and charebiasina . In factntiation ,64. SuchE. erythrinus, therefore it is only possible to compare our results with those from other species. The mean value of nucleotide diversity (π) for E. eryhtrinus was 0.00077, while studies in other fish species gave higher values, as in Nassau Grouper (0.3566 with RAD-Seq data) [H. malabaricus with DArTSeq data (0.001172) [This is the first study that has investigated genetic diversity in eq data) . Convers.001172) . It is iXYD and AD values), it is evident that groups A1 and A2 are very similar, while karyomorph D is much more different from these groups, with values at least ten times higher. All groups showed negative Tajima’s D values, which measures the difference between the mean number of pairwise differences and the number of segregating sites. There are three main hypotheses for this result: (i) purifying selection is removing variation from these groups and maintaining their genetic diversity stably ; (ii) genetic drift may be influencing the genetic diversity of these groups, mainly because they are sedentary which may favor the occurrence of inbreeding [Moreover, our results indicate that karyomorphs A and D are already highly differentiated in both chromosomal and genomic features. When considering the two differentiation metrics implemented here  tE. erythrinus presents sedentary behavior and is not a migrant species. Taking all these facts into account, even though the two sampling localities are in the same geographic basin, they are somewhat isolated. When comparing karyomorphs A and D, we have a different scenario, since they are geographically distant from each other and are also different in terms of the presence of sex chromosomes, which could accelerate the differentiation of these populations and potentially lead to speciation.The current results and the divergence patterns observed are probably explained by two main factors. The first is that samples from the same karyomorph are expected to be more similar, although that is not a general rule. The second factor is the distance between these groups and, consequently, the probable time since the isolation event occurred. All karyomorph A groups are found in the Paraná River Basin, although they are in very distant regions. Additionally, Y-axis. This indicates that there is a higher diversity within and a low difference between the localities for karyomorph A. The Bayesian non-spatial population structure approach fastStructure  and D (multiple ♀X1X1X2X2/♂X1X2Y system) were collected in sintopy (the same location) and presented high genetic differentiation. According to values of XYD, the genetic differentiation between syntopic individuals from karyomorphs A and D was equivalent to the genetic differentiation of long-distance allopatric populations of karyomorph A. Other studies indicated that the emergence of neo-sex chromosomes could have contributed to the increase in genetic diversity , as demonstrated in sticklebacks [Rumex hastatulus [Among erythrinids, it has been recently highlighted in another study that the presence of a multiple sex chromosome system may be associated with the speciation process . Hoplias systems , couplin process . In H. mklebacks  and in tstatulus . In all statulus .E. erythrinus both from cytogenetic and genetic points of view. Even though the genetic diversity indexes were globally low, karyomorphs A and D are highly differentiated genetically. In addition, three main hypotheses for the differentiation found can be proposed. (1) Long-distance allopatry could have been the main agent of genetic differentiation between these karyomorphs. (2) The emergence of neo-sex chromosomes may have been the main factor that promoted this genetic differentiation. (3) Both long distance allopatry and the emergence of the neo-sex chromosomes on karyomorph D may have contributed to the fixation and accumulation of divergences. Hypothesis 3 is our main hypothesis. In summary, our data provided an additional layer of evidence for the status of the taxon E. erythrinus and corroborated previous studies, supporting the conclusion that it is probably a species complex with an unresolved taxonomy. Future studies using high-throughput sequencing focusing on sex determination in this complex will help to highlight the possible link of sex chromosomes with the speciation process in this group.Our results indicated the presence of high genetic differentiation among populations of the species complex


Parastacus is a genus of South American freshwater crayfishes disjunctively distributed in southern Chile, Northern Argentina, Uruguay and Southeastern Brazil. Parastacus pugnax is a Chilean endemic distributed along 700 km of latitude in central-southern Chile from the Pacific coast to the Andean piedmont, which is intensively captured for consumption for local communities. Considering the habitat (wet meadows) and natural history  of the species, we tested a hypothesis of highly structured genetic diversity using mtDNA of 465 specimens gathered at 56 localities across the species range. The crown age of P. pugnax was estimated at 38 Ma, predating the main Andean uplift. The genetic variation of P. pugnax is large and geographically structured. In some cases, genetic groups do not match basin limits, suggesting a previous to current dynamic of basin evolution. The uncovered intraspecific main lineages have different demographic histories. A latitudinal cline in past effective population size reduction suggests environmental singularities with a glacial effect in the southern populations. We suggest adding morphologic and more genetic data in order to assess species limits. Our results contribute to improve future conservation actions for this taxon, providing basic information to delimit conservation units. Population differentiation and geographic arrangements of genetic diversity are influenced by many factors, including population age, historical population sizes, level of gene flow, selection regimes, and the history of landscape3. Current spatial patterns of genetic variation have largely resulted from the role played by habitat discontinuities e.g.4 and the historical stability of habitats, which in turn is influenced by episodes of past climate change6.Inquiring into the historical processes that have affected genetic diversification is essential to build solid approaches to explain current geographic patterns of intraspecific biodiversity7. Meanwhile, the Andean uplift was accentuated about 23 million years ago; the relief with heights similar to present ones was reached about 5 million years ago , often not connected to main watercourses, and which are generally highly fragmented and dependent on underground aquifers23.Species inhabiting superficial freshwater show distributions restricted by the limits of aquatic drainages and usually show patterns of genetic structure consistent with watershed boundaries e.g.,24, which diverged from its Laurasian counterpart in the Triassic, after the breakup of Pangea25. South American parastacids form a monophyletic group27 with a disjunct distribution. The largest phylogenetic diversity is concentrated in Chile where the three South American genera  are present, summing seven species. Then, 13 species of Parastacus are distributed within eastern South America in southeastern Brazil, northeastern Argentina, and Uruguay28. This distributional pattern suggests that the basal radiation of Parastacus predates the Andean uplift.The family Parastacidae (ca. 200 species) is a group present in South America, Madagascar, Australia, Tasmania, New Guinea, and New ZealandParastacus is represented by two endemic species29; see also30. Parastacus nicoleti (Philippi 1882) is distributed in the Coastal range of the Valdivian forest from the Tolten basin (ca. 39° S) to Hueyusca, Los Lagos, in the Río Bueno Basin (ca. 40° 55′ S). The second species is P. pugnax , the focal species of this study, which is parapatrically distributed to the north of P. nicoleti; it ranges from Nehuentue in La Araucania (ca. 39° S) to the Aconcagua river in Valparaiso (ca. 32° S), throughout a varied range of climates and a heterogeneous physiography , which resulted in 168 different haplotypes, in which 186 positions (29.57%) were variable. The result of the Xia test indicated low saturation levels in the set of COI sequences (Iss < Iss.c). The average haplotypic diversity (Hd) and the average nucleotide diversity (pi) were 0.98 and 0.061 respectively. Detailed values for genetic variation per locality are shown in Table P. pugnax form a highly supported clade (PP = 1.0). The Bayesian tree showed a latitudinally structured topology, with three main genealogical groups. The basal dichotomy of the gene tree leads to two main clades, which are allopatric  is widely distributed and includes haplotypes distributed from the northern Andean Mataquito basin to the coastal Paicaví basin in the south. Within clade I.2 Andean basins showed relationships with several coastal basins, and no reciprocal monophyly was detected at the level of such basins. For example, a southern clade within clade I.2 included some Andean haplotypes both from the Itata and the Biobío rivers, and coastal haplotypes from the coastal Carampangue basin. Within the southern clade II there are three highly divergent subclades; one corresponding to variants from locality 36 of Los Sauces (at the southern edge of the Biobío basin), a second including haplotypes from specimens collected at Traiguén , and the third one includes sequences from the rest of the Imperial basin and the coastal site of Puerto Saavedra . Another set of barriers suggests low historical gene flow between Biobío, Imperial and southern coastal basins (ca. 38° S). From the AMOVA, the values of genetic differentiation were significant for the three partitions (p < 0.001), suggesting high structuring at geographic scales, but the largest fraction of the genetic variation corresponds to comparisons among groups . The lowest genetic variation was for the within localities hierarchy 17. However, no study has focused on the specialists of a widely distributed type of habitats as are the subterranean waters  of central and south central Chile. This study is the second30 phylogeographic analysis of a Chilean species of Parastacus, a genus of South American endemic subterranean crayfishes. We found that P. pugnax is composed of lineages whose age precedes the uplift of the Andes; the old genetic lineages encompassed in this taxon would have arisen by the late Eocene. We also detected high levels of geographic structuring of the species genetic diversity, and we found signals of population size changes, which increased in magnitude towards the southern range of the species. Below we discuss these findings in detail.The Chilean biota has been at the center of several phylogeographic studies. These have focused on marine e.g.,P. pugnax , whose geographic boundaries could be explained by alluding to both climatic factors and relief , from the rest of the populations from Biobío to the north. Phylogeographic breaks at a similar latitude have been reported for other taxa, such as the red cricket, Cratomelus armatus35, and the lizards Liolaemus tenuis and L. pictus11, with divergences that, in some cases, could warrant the recognition of different species. Given that the biology of such taxa is different, and the divergence times are much more recent than that of Parastacus, we suggest that the drivers of evolutionary differentiation at such latitude would have occurred recurrently, during the Cenozoic. This break is concordant with the bioclimatic transition between the Wet Mediterranean zone in the north and the wetter Valdivian zone in the south and one of the main biogeographic transition zones of Chile37, suggesting the confluence of several factors that have reduced the permeability of this complex of barriers, promoting the evolutionary differentiation at different taxonomic levels. At this latitude, the Intermediate Depression is interrupted by mountainous elevations, and three deep fluvial canyons that also interrupt the plane of the habitat of P. pugnax. The effects of the glaciations were of greater intensity towards the south of South America, where they also affected the low altitude areas. In this context, the effect of the glaciations, would have been larger at latitudes where the southernmost clade of P. pugnax is distributed38. Similarly, a synergistic effect of mountains and rivers, Cordillera Cantillana and Maipo River , although the basins are not reciprocally monophyletic. Likely, events of river capture may explain the observed pattern of non-monophyletic basins43.According to previous phylogeographic studies, aquatic continental organisms tend to have their genetic diversity more structured than other organisms e.g.,P. pugnax is a species with low vagility and direct development28, traits that likely contribute to mold the uncovered patterns of high genetic differentiation at a small geographical scale. Although there is a significant positive relationship in the analysis of isolation by distance 44; emerged land may have served to connect nearby but today disconnected basins that reach the ocean. The LGM-ENM model indicates that P. pugnax expanded into currently submerged environments, extending its range to the west of the current coastline. This scenario has been suggested to explain observed patterns of genetic variation of Australian45 and Argentinean21 fishes. However, the Chilean continental shelf is narrow, which would have made the connection between neighboring coastal basins unlikely, except for those associated with larger plains, such as Carampangue and Lebu, for which we found shared haplotypes. As such, our results are concordant with those found for the eastern Australian species of Euastacus that show high levels of genetic differentiation among a series of small coastal basins40.nce Fig. , in compnce Fig. ; in some49, where changes in distributional ranges and Ne would have been stronger at higher latitudes. Our results suggest that P. pugnax was affected by the glaciations, with the effect being larger south of the Biobío-Nahuelbuta (clade II). Although the ENM modeling suggests large habitat suitability changes for the LGM, the estimated reductions of Ne were not associated with the last glaciation, as they are much older. In South America, glaciations have been a cyclical process during the last 6 My9. For P. pugnax the reductions in distribution would have been of greater magnitude in the interior valley of the Intermediate Depression than in coastal areas, which would not only have been climatically more stable, but also would have increased in extension as sea level decreases. However, we did not find clear signals of greater variability, expected for historically stable populations, in the coastal basins. This suggests additional factors, not considered here, that would have reduced coastal population sizes. One of these factors is likely basin size, as larger basins tend to harbor more genetic diversity (when considered as pi), which in a scenario of neutrality suggests that larger basins would harbor larger populations. In this regard is of interest to note the likely effect of repeated marine transgressions, to which the lowlands near the Pacific coast would have been more exposed, than the interior environments of the Andean basins.The climatic changes associated with glaciations have produced considerable effects on the Chilean biota e.g.,P. pugnax suggests the existence of cryptic specific diversity that should be further analyzed with additional markers and the analysis of morphological variation. The existence of cryptic diversity in the western slope of the southern Andes, which is starting to be uncovered, seems to be widespread among taxonomic groups e.g.,51. Similarly, several studies have delimitated morphologically species in the family Parastacidae. For example, the existence of more than one species was recently proposed within the Australian Cherax destructor52. This suggests that parastacids, due to their age and biological attributes, would be prone to speciate without much morphological variation. The scarce exophenotypic differentiation in underground organisms, such as primary burrowing crayfishes, has been explained in terms of the restrictive and homogeneous conditions of the subterranean microhabitat, which would cause morphological stasis54. The little phenotypic variation displayed by some species of Parastacus is evident when comparing, for example, the species of Brazil and Chile, which would have diverged more than 40 Mya. A genealogical study suggested the existence of cryptic speciation in P. brasiliensis42. In agreement with this, six new Brazilian species of Parastacus have been described in the last five years supported largely by genetic distinction58, which suggests that the real species richness of the genus is far from being fully detected. As such, the scenario that we are uncovering for the Chilean P. pugnax is not unexpected.The existence of ancient clades within P. pugnax uncovered here, correspond to three distinct species, some interesting taxonomic issues emerge. Two nominal forms are synonymized under P. pugnax. One is P. chilensis  with type locality in Valparaiso, a locality lying in the distribution of the northern clade I.1. As such, the name P. chilensis is available for this lineage. The other taxon now synonymized under P. pugnax, is P. hassleri  with type locality in Talcahuano, which is also the type locality of P. pugnax. As such P. pugnax, with P. hassleri in its synonymy, applies in a restricted sense to the large central clade (I.2). Remarkably, no name is available for the southern lineage (clade II); as such, if this lineage is to be considered at the species level, a new species should be described and named to encompass it.Noting that no taxonomic innovation is advanced in this study, if further studies corroborate that the three main mitochondrial lineages of P. pugnax sensu lato. First, the genetic diversity of P. pugnax suggests the existence of several ESUs59 that should be treated as separate conservation units. For example, the southern clade II, although younger, presents three allopatric clades  that diverged more than 20 Mya. Several other clades are considerable old and also deserve conservation. In addition, the fact that almost each vega  is monophyletic and possesses private haplotypes, confers high singularity on a small spatial scale. In this regard, special attention should be placed in the area of greatest extraction of the species, which corresponds to the range of the large central (I.2) and southern (II) clades. According to some statistics, more than 1500 ton are annually extracted, both during crayfish festivals and by people who use this species as a subsistence resource every winter31. From this point of view, P. pugnax is not only an evolutionary unique and genetically diverse species, but it is a valuable resource of cultural importance that is relevant to conserve.Our results have relevant implications for the conservation of Parastacus (see30) and provides evidence of great ancient diversity within one of the two currently recognized Chilean species of the genus. The uncovered phylogeographic pattern indicates an important role of the long-standing orogenic and climatic processes, which would have shaped the distribution of the high genetic diversity of the species, at the time that provides the basis for future studies integrating more genes and morphological evidence. Such studies would, for example, evaluate the distinction at the species level of the main lineages here uncovered. Our results are also a call of attention to ensure the conservation of the genetic diversity of P. pugnax, an intensely exploited resource.This is the second phylogeographic work for P. pugnax. Specimens are housed in the Museum of Zoology of the University of Concepción, Chile. Crayfishes were sampled with the Servicio Agrícola y Ganadero (SAG-Chile) Permit Resolution N° 2687, and Bioethics Certificate VRID-Universidad de Concepción. Chile. Sampling sites were classified into three groups, corresponding to the main Chilean bioclimatic zones as follows  and five have headwaters in the Andes, running westwards and receiving distinct coastal drainages as tributaries before reaching the ocean  gene. Polymerase chain reactions (PCR) were performed in a total volume of 25 µL containing 12.5 µL Thermo Scientific PCR Master Mix, 0.2 µM of each primer and 20 ng of template DNA. PCR reactions were performed under the following conditions: an initial 2 min step at 95 °C, followed by 35 cycles with 45 s at 95 °C, 45 s at 50 °C, 1 min at 72 °C, and a final extension of 5 min at 72 °C. Amplicons were sequenced at Macrogen Inc., Seoul, Korea. Sequence editing was done using Codon Code Aligner v. 3.0.3 (Codon Code Corporation 2009); sequences were translated into amino acids in order to verify the absence of stop codons and reading frame shifts. New sequences were deposited into Genbank (MW135536-MW136000).DNA was extracted from muscle tissue (first pereiopod) using a commercial kit  following the instructions of the manufacturer. The primers LCO-1490 (5′-GTCAACAAATCATAAAGATATTGG-3′) and HCO-2198 (5′-TAAACTTCAGGGTGACCAAAAAATCA-3)61. Descriptive statistics were calculated both for sampling localities and river basins. Genetic diversity was estimated in terms of the number of haplotypes (H), haplotype diversity (Hd), and nucleotide diversity (Pi) using Arlequin 3.562. Average genetic divergence (including standard errors) among basins was estimated based on p-distances using Mega 663. In order to estimate the association of geographic distance between sampling localities and genetic distance (p-distance), we estimated the correlation between both variables. This analysis was done in Primer v.7.0, with the Relate option64. To assess if there is an association between the basin size and genetic diversity (Hd and pi) a Spearman non-parametric correlation was performed using the option Relate in Primer v 7.0, with 10,000 permutations (https://www.primer-e.com/). This analysis correlates, using a Spearman's non-parametric method, hemimatrices of basin size and hemimatrices of genetic diversity values.Sequence alignment was done using Codon Code Aligner v. 3.0.3. The magnitude of nucleotide substitution saturation for the gene was tested using DAMBE software65 of COI gene sequences was constructed to visualize the relationships among haplotypes using PopArt 1.7 (http://popart.otago.ac.nz). In addition, a gene tree was constructed using Bayesian inference (BI) in MrBayes 3.0b466. We added sequences of the Brazilian P. brasiliensis (KU258636) and P. pilimanus (FJ966039) to root the tree, assigning it the condition of sister species of P. pugnax25. The Akaike’s information criterion (AIC) and Bayesian information criterion (BIC)67 implemented in jModelTest 268 indicated the T92 + G + I as the best-fit model for the COI data. Two independent Bayes runs during 6,000,000 generations each were performed and sampled every 1000 generations. Each run consisted of four Monte Carlo Markov chains (MCMC). After discarding the first 15,000 samples as burn-in, the remaining trees were used to create a 50% majority rule consensus tree with posterior probability (PP) values.A median-joining (MJ) networkP. pugnax, the nucleotide substitution rate for the Cytochrome Oxidase I (COI) region of the Parastacidae family was obtained with BEAST v1.8.369 using COI gene sequences of 91 species from South America, Madagascar, New Zealand, and Australia, obtained from Genbank, plus samples of the Parastacidae family from Chile obtained by us in this study .A strict molecular clock at species level was calibrated using the first fossil appearance of the families Parastacidae,Chimerastacidae, Astacidae, and Cambaridae (see details in Table 2 of ). The an72. We tested values for K in the range of 2–17 (according to the maximum number of basins); starting condition was set to 100 with 10,000 iterations. The best configuration of population groups, which was the one retained, show the highest Fct value and less than 50% of clusters composed of a single population72.A spatial analysis of molecular variance  was conducted to find spatially homogeneous and highly differentiated locality groupsP. pugnax using Monmonier’s maximum difference algorithm implemented in BARRIER 2.273. BARRIER uses a triangulation network to connect contiguous populations by means of a Voronoi tessellation set to detect probable barriers to gene flow among differentiated populations73. Each edge of the Voronoi polygons was associated with the value of the corresponding Fst value between pairs of populations, calculated with ARLEQUIN. The method highlights the limits of the Voronoi tessellation associated with the highest values of Fst between pairs of populations, thus identifying multiple barriers in the hierarchical progression of Fst values. We only considered genetic discontinuities greater than the general average of the Fst between all population pairs.In addition, we inferred the presence of historical genetic barriers among the populations of P. pugnax was conducted by an analysis of molecular variance (AMOVA)74 using Arlequin. Haplotypes were grouped by sampling localities and basins.A hierarchical analysis of the spatial arrangements of genetic diversity of P. pugnax using the Bayesian Skyline Plot (BSP) method implemented in BEAST75. Piecewise-constant Skyline Model was selected, and a relaxed uncorrelated lognormal molecular clock was applied with the mutation rate obtained from our calibration (3% per My). We ran 10,000,000 generations and discarded the first 10% as burnin. Distributions were used to generate confidence intervals corresponding to the uncertainty of the coalescence process76; we used Tracer 1.777 to monitor parameters estimates and ESS values.In order to assess patterns of historical demographic changes, we estimated the departure from neutrality statistic Fs , as implemented in Arlequin 3.5. This index was estimated for each locality, as well as for each basin. Similarly, historical population sizes were estimated for each main clade of 78. We downloaded contemporary (1950–2000) and LGM (~ 21Ka) climate data at a 2.5 arc-minutes resolution (nearly 5 × 5 km) from WorldClim79. Additionally, a more accurate (1 km resolution) distribution model was generated for the present using elevation data from the NASA Shuttle Radar Topography Mission (SRTMv4.1)80 and bioclimatic variables from WorldClim. Furthermore, given the ecological features of P. pugnax, we used soil parameters at 1 km resolution and 1 m depth, as bulk density, clay content, volumetric percentage of coarse fragments, silt content, sand content, soil pH, and soil organic carbon stock81. Pearson’s correlation coefficients were estimated for each pairwise comparison of variables using ENM Tools v 1.4.382; highly correlated variables (Pearson’s coefficient 0.9) were removed from analysis. We performed a priori model testing to determine optimal combinations of the regularization and feature parameters for the construction of ENMs83. We tested model combinations of regularization parameters from 1 to 10 in intervals of 1 and feature classes. Model performance under different combinations of these settings was evaluated using AIC as optimality criterion to protect from overfitting. The R package ENMeval v 0.1.1 . The combination of feature class (FC) and regularization multiplier (RM) was selected for subsequent analyses.To estimate the potential current distribution and identify probable refugia during the LGM, environmental niche models (ENMs) were generated with Maxent 3.3.3eSupplementary Information.


Luffa acutangula (ridge gourd) accessions, mainly from Thailand. A total of 2834 SNPs were used to establish population structure and underlying genetic diversity while exploring the fruit characteristics together with genetic information which would help in the selection of parental lines for a breeding program. The study found that the average polymorphism information content value of 0.288 which indicates a moderate genetic diversity for this L. acutangula germplasm. STRUCTURE analysis (ΔK at K = 6) allowed us to group the accessions into six subpopulations that corresponded well with the unrooted phylogenetic tree and principal coordinate analyses. When plotted, the STRUCTURE bars to the area of collection, we observed an admixed genotype from surrounding accessions and a geneflow confirmed by the value of FST = 0.137. AMOVA based on STRUCTURE clustering showed a low 12.83% variation between subpopulations that correspond well with the negative inbreeding coefficient value (FIS =  − 0.092) and low total fixation index (FIT = 0.057). There were distinguishing fruit shapes and length characteristics in specific accessions for each subpopulation. The genetic diversity and different fruit shapes in the L. acutangula germplasm could benefit the ridge gourd breeding programs to meet the demands and needs of consumers, farmers, and vegetable exporters such as increasing the yield of fruit by the fruit width but not by the fruit length to solve the problem of fruit breakage during exportation.This study explored a germplasm collection consisting of 112  Luffa acutangula, commonly known as ridge gourd, angled loofah, or Chinese okra, is a domesticated vegetable of the Cucurbitaceae originating from India4. Immature fruits use as a vegetable, which can be cooked or fried. In South-East Asia, sweet juiciness and soft texture are preferred characteristics2. Its use in traditional medicine is especially prevalent in Asia, middle America6, and India to treat jaundice and urinary bladder stones8. Ridge gourd production is of great importance to smallholder farmers and exporters in Asia. People in specific areas demand different ridge gourd fruit types. Nowadays, commercial ridge gourds have very long fruit that causes the fruit to break during packing. As the demand for ridge gourds in Asia9 steadily increases, the vegetable will be put through further selection by breeding programs leading to decreased genetic diversity, as documented in many crop species like durum wheat10. To prevent the loss of diversity, creating a germplasm that contains natural genetic resources will be of paramount importance in future breeding initiatives for introgressing biotic or abiotic stress tolerance or resistance factors into elite cultivars.11. Still, such a limited approach is subject to environmental as well as developmental conditions of the plant12. Instead, molecular markers provide stabler sources of information for carrying out genetic diversity studies and are more suitable for establishing genetic diversity in and between populations. Identifying population structure is essential for understanding the genetic diversity, evolutionary forces, and geographic distribution of the germplasm13. This information would be of great help to plant breeders in selecting potential gene pools in breeding programs14.Most of the genetic diversity in germplasms have been established by describing the morphological variation of the accessions15. Molecular markers used for population structure and genetic diversity studies in ridge gourd include Inter Simple Sequence Repeat (ISSR)16, Random Amplified Polymorphism Detection (RAPD)17, Simple Sequence Repeats (SSRs)18, and Directed Amplification of Minisatellite DNA (DAMD)19. These techniques can be labor-intensive, costly and produce a low number of markers. Newer genotyping by sequencing (GBS) methods can cut down genome complexity and provide important genotype information. An example is the DArTseq method that can select the genome fractions corresponding to active genes23.Various marker techniques have been developed for acquiring accurate and reliable information on population structure and genetic diversity of germplasmsL. acutangula germplasm in Thailand using DArTseq based SNPs. The study will provide essential information for Luffa improvement programs for breeders worldwide for future on-farm problems.The present study aims to identify population structure, genetic diversity, and association of fruit traits with subpopulations of a L. acutangula has an estimated genome size of 760 Mb, spread over 13 chromosomes. It has approximately 42,211 predicted gene models, of which 32,233 are protein-coding genes with an average gene size of 2886 nt22. The 112 L. acutangula accessions of this study originated from two genetic resources. Based on the analysis of 2834 SNPs, we observed significant intra-chromosomal SNP pairs (p < 0.05) with an average r2 of 0.217 and a mean distance of 11,824,426 nt. STRUCTURE software used a model-based Bayesian algorithm to infer population structure (K > 1). The actual number of clusters (K) was determined by the ad hoc statistic ∆K based on the log probability of data with respect to K values24. STRUCTURE analysis of the 2834 SNPs reveals the highest value of ΔK at K = 6  and observed heterozygosity (HO). HE gives information about the probability of an individual’s portion of heterozygosity for all analyzed loci, while HO is the portion of heterozygous genes in the analyzed population25. The HE as measure of genetic diversity ranges from 0.338 (Cluster 2) to 0.272 (Cluster 3) in the six clusters; the remaining cluster values are the following: Cluster 1 (0.274), Cluster 4 (0.295), Cluster 5 (0.287) and cluster 6 (0.334)  of 4.127. The HO for the STRUCTURE grouping is as follows: Cluster 1 (0.378), Cluster 2 (0.388), Cluster 3 (0.191), Cluster 4 (0.289), Cluster 5 (0.363) and Cluster 6 (0.410)  is lower in value than the HE (0.295). Cluster 3 has the lowest HE (0.272) and HO (0.191) from all six subpopulations. The mean SNP polymorphism information content (PIC) has a range of values from 0.125 to 0.375, with a mean of 0.288 (Table FST = 0.137) and in the total fixation index (FIT = 0.057), while a low fixation index occurs within populations (FIS =  − 0.092) (Table Heterozygosity can be measured by expected heterozygosity (FST = 0.262), while the lowest value occurs between Cluster 1 and Cluster 2 (FST = 0.089), and also between Cluster 2 and Cluster 3 (FST = 0.086) (Table 27. AMOVA based on STRUCTURE clustering shows 12.83% variation among subpopulations and a negative AMOVA variance within subpopulations (− 10.59%)  were found in accessions collected from Southern Thailand. Also, the main admixed proportion making up accessions from Vietnam is closely shared with accessions from the Philippines and Indonesia. This suggests a close relationship between people from these countries. In the past, this part of Southeast Asia may have shared a common ancestral history with the possibility of immigrations among mainland (Vietnam) and islands in southeast Asia (the Philippines and Indonesia) which probably lead to the exchange of goods between these countries in the past32. The observed distribution of a subpopulation (Cluster 4) to the southern part of Thailand point at the distribution of seeds along historic commerce ship routes from the Southern Thailand regions to countries such as India and China33. Peculiarly, the leading representative of a subpopulation (Cluster 5) is from the USA, perhaps collected somewhere from Asia, as Luffa is native to Asia and not the Americas2. The last subpopulation (Cluster 6) was made up equally of accessions from Thailand and Bangladesh with a genotype characteristic which might have been introduced from the Nepal region via Bangladesh34, but due to the lack of samples from Myanmar, confirmation of the trafficking of this material is difficult35 25 occurs in four subpopulations  (Table HO < HE) caused by isolation due to barriers such as the mountainous area of Laos and Thailand and that of the island countries25. A closer look at the genotypes of ridge gourd in Thailand (Cluster 1) shows that the spreading of a subpopulation representative specific SNPs occurs throughout accessions that were collected nearby the area  with accessions from Thailand, Vietnam, Indonesia, and the Philippines favor long fruits with a wide range of fruit shapes Fig. . The lonEach of the distinct clusters will prove a valuable source for selecting promising parental genotypes regarding fruit length and fruit shape characteristics. Choosing such material from different clusters will help maximize genetic diversity while efficiently meeting demands from consumers and producers. These subpopulations will also play a vital role in breeding programs to mitigate future environmental limitations such as resistance to plant pathogens and adjustment to a constantly changing climate.L. acutangula germplasm. Phenotypic information, such as the fruit length and fruit shape, displayed a corresponding variation according to the six populations inferred by the genotypic data. At the same time, geographical provenance was reflected in the clustering analysis, which also showed a relationship to the phenotypic information. This moderate diversity serves as a solid catalyst for utilizing such germplasm for breeding programs geared towards the farmer, consumer and vegetable exporter preference. The wide-ranging fruit lengths and shapes will help solve current ridge gourd exporter’s problems in Thailand. One main problem faced by ridge gourd exporters is fruit breakage during packaging caused by very long fruits. Farmers tend to grow long fruits to get a higher weight or yield.The study found significant genetic diversity in Thailand L. acutangula germplasm comprising of 112 accessions from Thailand (91), Vietnam (4), Philippines (2), Indonesia (1), China (3), USA (1), Laos (3) and Bangladesh (7), conserved by the Tropical Vegetable Research Center (TVRC), Kasetsart University, Kamphaeng Saen Campus, and the World Vegetable Center, Taiwan  containing 2 μg/mL RNase. DNA quality was evaluated by electrophoresis on a 1% agarose gel and was quantified with a NanoDrop 2000c spectrophotometer V 1.6.0. The DNA concentration was adjusted to 50 ng/μL for DArTseq GBS analysis.Genomic DNA samples were extracted from 100 mg of pooled young leaves tissue of 2-weeks-old seedlings from 20 plants per accession using a modified cetyltrimethylammonium bromide (CTAB) method38. To this end, DNA was digested using PstI-MseI restriction enzymes as described by Kilian20. The digested fragments were then ligated to adapters and amplified by PCR21, followed by sequencing on Illumina Hiseq2000. The single read sequencing was run for 77 cycles, and sequences generated were handled by DArT analytical pipelines . In the primary pipeline, poor-quality sequences were filtered from the FASTQ files by applying rigorous selection criteria to the barcode region39. Identified sequences per barcode/sample were used for marker calling. These files were then used in the secondary pipeline for DArT P/L’s proprietary SNP calling algorithms (DArTsoftseq).The genomic DNA samples were sent to Diversity Arrays Technology Pty. Ltd., Canberra, Australia, for DArTseq genotype-based sequencingL. acutangula accessions was determined using STRUCTURE version 2.3.423. Ten repeats were performed for each number of hypothetical subpopulations (K) which were set from 1 to 10. The parameters used consisted of an admixture model, a burning period of 50,000 steps, and 100,000 Markov Chain Monte Carlo (MCMC). The STRUCTURE results were further analyzed and visualized using the r package, pophelper version 2.3.040. The optimum number of K was calculated using the Evanno method24.DArTseq based SNPs were filtered using a call rate of 80% with a co-dominant marker polymorphism information content (PIC) greater than 0.125. After filtering, 2834 SNPs were used for data analysis. The population structure of the 112 41 and visualized the data with DARwin software version 6.0.02142. Principal Coordinates Analysis (PCoA) graphs were created in dartr version 1.9.143, expected heterozygosity (HE), observed heterozygosity (HO), and pairwise FST were calculated using adegenet version 2.1.144 in the R statistical environment45. Differentiation among populations (FST), total fixation index (FIT ), and fixation index within populations (FIS ) were calculated using the r46 package hierfstat version 0.5–747. Analysis of Molecular Variance (AMOVA) and Shannon-Weiner Diversity index were calculated in poppr version 2.8.3 in r. Jaccard distance was calculated in r software by using ade4 version 1.7–13 package48. One-way ANOVA test, box plots and Tukey HSD were carried out using standard r statistics. Linkage disequilibrium (LD) for SNP markers was calculated using TASSEL v.5.049.We constructed the phylogenetic dendrograms with the weighted neighbor-joining methodLuffa acutangula accessions were used according to the Standard Material Transfer Agreement (SMTA) from the World Vegetable Center (AVRDC) number SMTA-00AD43-00AV74-180920, and from the Tropical Vegetable Research Center (TVRC) number SMTA-62/033. Experimental research and field studies on plants did comply with relevant institutional, national, and international guidelines and legislation as well as SMTA guidelines.112 Supplementary Information.


The mitochondrial DNA (mtDNA) D-loop of endangered and critically endangered breeds has been studied to identify maternal lineages, characterize genetic inheritance, reconstruct phylogenetic relations among breeds, and develop biodiversity conservation and breeding programs. The aim of the study was to determine the variability remaining and the phylogenetic relationship of Martina Franca , Ragusano , Pantesco , and Catalonian (CT) donkeys by collecting genetic data from maternal lineages. Genetic material was collected from saliva, and a 350 bp fragment of D-loop mtDNA was amplified and sequenced. Sequences were aligned and evaluated using standard bioinformatics software. A total of 56 haplotypes including 33 polymorphic sites were found in 77 samples . The breed nucleotide diversity value (π) for all the breeds was 0.128 . Principal components analysis grouped most of the haplogroups into two different clusters, I  and II (including haplotypes from MF and RG only). In conclusion, we found that the primeval haplotypes, haplogroup variability, and a large number of maternal lineages were preserved in MF and RG; thus, these breeds play putative pivotal roles in the phyletic relationships of donkey breeds. Maternal inheritance is indispensable genetic information required to evaluate inheritance, variability, and breeding programs. Equus is the only remaining member of the family Equidae, which includes both extant and fossil species [Equus africanus; zebras Equus quagga (formerly Equus burchellii), Equus grevyi, and Equus zebra (with two subspecies Equus zebra zebra of South Africa and Equus zebra hartmannae of Namibia and Angola); and the Asian wild asses Equus kiang and Equus hemionus (with subspecies Equus hemionus kulan and Equus hemionus onager) [E. africanus is accepted as a subspecies of the African wild ass [The genus  species . The non onager) ,3. The dwild ass ,5.2), and Catalonian (CT) of the Spanish Catalonian region, share similar phenotypes due to genetic inheritance/ecological constraints and the outcomes of biodiversity conservation programs. The natural history of the donkey is of interest because the desire to safeguard genetic biodiversity is growing [Geographically isolated donkey populations, referred to as breeds here, including Martina Franca (MF) of the continental Puglia region, Ragusano (RG) of the island province of Sicily, Pantesco (PT) of the small island of Pantelleria  and Clade 2 for the Somali lineage . These lineages resulted from two separate domestication events among two wild ancestral populations located in (1) the Atbara region and Red Sea Hills (NW Sudan) and in (2) southern Eritrea, Ethiopia, and Somalia [Interestingly, two lineages of the domestic donkey were identified using mtDNA: Clade 1 for the Nubian lineage , and haplotypes were identified and analyzed to investigate the matrilineal assortment within and between asinine breeds with similar phenotypes and to investigate the origin and phylogenetic relationships between asinine breeds to better manage rare donkey breeds by establishing proper breeding and conservation programs.One-hundred and twenty-three salivary samples were collected from eight official breeding stations , whereashttp://bioinfo.ut.ee/primer3, accessed on 4 April 2019). After extraction from the FTA mini-card, DNA was amplified by PCR in a 25 μL reaction containing 50 ng of DNA, 2.5 mM MgCl2, 0.2 mM each dNTP, 0.5 μM PER 5′- CC AAG GAC TAT CAA GGA AG-3′ and FOR 5′-TTG GAG GGA TTG CTG ATT TC-3′ primers, 1× PCR buffer, and 1 U of Taq DNA polymerase . The amplification was performed using the Mastercycler thermal cycler  with the following conditions: initial denaturation at 94 °C for 5 min followed by 35 cycles of 94 °C for 30 s, 58 °C for 30 s, 72 °C for 30 s, and then, a final extension at 72 °C for 5 min.Genetic material was collected from saliva using a sterile oral swab, transferred to FTA mini-cards, and stored in multibarrier pouches . The reference material is available at the O.V.U.D.  Centre for the breeding of large animals at the Faculty of Veterinary Medicine, University of Teramo, Italy. Based on the complete donkey mtDNA sequence (GenBank X97337) , two paihttp://www.technelysium.com.au/, accessed on 4 April 2019). Sequences were aligned with Muscle; in The raw sequence trace files were checked for the presence of ambiguous bases using the software Chromas v.2.5.1  to highlight the presence of SNPs. The absolute number of mutations identified for each sequenced sample is shown . Moreovep = 0.98). Maximum composite likelihood estimates of the nucleotide substitution pattern per breed, positions containing gaps, and missing data were eliminated as recommended by the literature ; 19 in RG ; 9 in CT ; and 3 in PT . The most represented seven haplotypes were Hap 51, common to all breeds; Hap 22, 36, and 37 found in MF and shared with PT and/or RG; Hap 30, distinctive of MF and also found in CT; and Hap 40 and 53, characteristic of CT.E. kiang, E. hemionus, and E. h. kulan lines  and RG , while the rest of the haplotypes are grouped into cluster I. However, six haplotypes are not included in clusters I and II: they are MF Hap 22, 37, and 40; and RG Hap 13, 16, and 38 .Donkey breeds represent a fascinating model of domesticated biodiversity; thus, a number of studies have analyzed donkey pedigree and genetics. Pedigree and reconstruction studies usually lack the corresponding genetics ,48,49, an = 62) and because of its isolation. In the PT certificates of origin, eight distinct maternal lineages are attested, which is in agreement with the statement that the recovery of the PT breed started from a small nucleus of founders [Pedigree studies on Italian  and Spanish  donkeys found dramatic losses in genetic variation due to high rates of inbreeding ,48,49,51founders . Furtherfounders ,49,53. BThe molecular characteristics analyzed showed distinct nucleotide frequencies among breeds, which is in line with the literature ,30. The We found the highest diversity in MF followed by RG and the lowest variability in CT followed by PT. The differences in our results and a previous study on the Italian donkey could be due to the following biases: (i) a lack of certificate of origin information leading to uncertain breed origin, (ii) unbalanced sampling between breeds with 74% of the breeds being Asinara and Sardo donkeys, both from the island of Sardinia . OverallThe population analyses showed more differences within RG, followed by MF, CT, and PT. The RG genetic population structure is analogous to the highly heterogeneous large maternal Balkan donkey population with a more complex genetic structure than previously thought . The BalOur analysis of PT is consistent with a microsatellite genetic variability study that showed lower variability in PT compared to RG and another Sicilian breed, the Grigio Siciliano (GS) . The genTwo different studies on the Balkan donkey revealed different interpretations. In the first study, no correspondence between geographical areas and maternal genetic structure was found. Because the difference between the Balkan donkey and the African Burkina Faso donkey outgroup was also low, the authors could not trace routes of expansion in the donkey; consequently, they suggested that the species spread very quickly after domestication . The secThe well-established identification of two main lineages and the probable existence of another unrecognized extinct wild ancestor in domestic donkeys are believed to be the result of separate domestication events ,35,36,37There is a rising interest in maintaining genetic diversity in animal populations to safeguard the widest possible genetic resources through conservation programs ,58.For conservation of domestic breeds, the preservation of genetic capital is crucial because they are already zootechnical forms with reduced original natural variability. The biodiversity of the domestic form is the result of a genetic pool derived from interaction with semi-artificial environments regulated by human needs and human migratory movements. As a result, genetic and phenotypic changes with respect to the wild species of origin have been addressed slowly based on human necessity.Donkey conservation represents a biological problem with regard to analogous phenotypes. The similar donkey breed phenotypes may be a result of genetic exchange among breeds, identical origin from an African or Asian population, equivalent climate conditions, and/or similar types of work carried out by the donkeys.Our molecular analyses showed a dramatic loss in variability across all breeds tested, but especially in PT and CT. MF and RG have higher numbers of haplotypes and SNPs than PT and CT. In practice, the MF donkey is an important reservoir of biodiversity that must be preserved with the widest possible range of its genetic heritage, and errors of consanguinity for aesthetic purposes should be avoided with the help of conservation programs. Future conservation programs should include certificates of origin and genetic analyses of the matrilines at least.In conclusion, this study: (i) identified D-loop mtDNA characteristics for the MF donkey and three phenotypically similar breeds, (ii) identified different matrilines in the MF donkey and similar breeds, (iii) identified the biodiversity of each breed, and (iv) determined the phylogenetic relationships among the breeds. This extensive study on the biodiversity and phylogenetic relationships of MF, RG, PT, and CT donkey breeds is useful for future domestication studies.In this study, we analyzed the genetics of a limited number of endangered and highly endangered donkey breeds. The data showed significant loss in variability among all the breeds evaluated, which is in agreement with previous studies that used different methods. The primeval haplotypes, haplogroup variability, and large number of maternal lineages are preserved in the MF and RG breeds; thus, they play putative pivotal roles in the phyletic relationships of the studied donkey breeds. Given the level of endangerment undergone by these breeds, actions are necessary to ensure their long-term survival and conservation. Improving the reproduction and management of existing populations, clarifying their historic interactions by studying their population genetics, and extending and improved monitoring of maternal lineages are valid options.