Glossina morsitans morsitans originating from a colony at the Seibersdorf rearing facility. The genome organization and the phylogenetic analysis based on the RNA-dependent RNA polymerase (RdRp) revealed that the two viruses belong to the genera Iflavirus and Negevirus, respectively. The names proposed for the two viruses are Glossina morsitans morsitans iflavirus (GmmIV) and Glossina morsitans morsitans negevirus (GmmNegeV). The GmmIV genome is 9685 nucleotides long with a poly(A) tail and encodes a single polyprotein processed into structural and non-structural viral proteins. The GmmNegeV genome consists of 8140 nucleotides and contains two major overlapping open reading frames (ORF1 and ORF2). ORF1 encodes the largest protein which includes a methyltransferase domain, a ribosomal RNA methyltransferase domain, a helicase domain and a RdRp domain. In this study, a selective RT-qPCR assay to detect the presence of the negative RNA strand for both GmmIV and GmmNegeV viruses proved that both viruses replicate in G. m. morsitans. We analyzed the tissue tropism of these viruses in G. m. morsitans by RNA-FISH to decipher their mode of transmission. Our results demonstrate that both viruses can be found not only in the host’s brain and fat bodies but also in their reproductive organs, and in milk and salivary glands. These findings suggest a potential horizontal viral transmission during feeding and/or a vertically viral transmission from parent to offspring. Although the impact of GmmIV and GmmNegeV in tsetse rearing facilities is still unknown, none of the currently infected tsetse species show any signs of disease from these viruses.Tsetse flies cause major health and economic problems as they transmit trypanosomes causing sleeping sickness in humans  and nagana in animals . A solution to control the spread of these flies and their associated diseases is the implementation of the Sterile Insect Technique (SIT). For successful application of SIT, it is important to establish and maintain healthy insect colonies and produce flies with competitive fitness. However, mass production of tsetse is threatened by covert virus infections, such as the Glossina pallidipes salivary gland hypertrophy virus (GpSGHV). This virus infection can switch from a covert asymptomatic to an overt symptomatic state and cause the collapse of an entire fly colony. Although the effects of GpSGHV infections can be mitigated, the presence of other covert viruses threaten tsetse mass production. Here we demonstrated the presence of two single-stranded RNA viruses isolated from  Hytrosaviridae) [Tsetse flies are vectors of trypanosomes, causative agents of sleeping sickness in humans and nagana in livestock. Although only 8–10 tsetse species are of medical and/or economic importance, there are about 31 tsetse species and sub-species distributed in 37 sub-Saharan African countries [viridae) . GpSGHV viridae) . This jeviridae) ,6.Wigglesworthia glossinidia, Sodalis glossinidius, Wolbachia pipientis and Spiroplasma—which may contribute to the digestion of nutrients in the flies as well as regulate fly reproduction and immunity. These findings indicate interdependence of the symbionts and GpSGHV which may influence GpSGHV transmission and pathologies [In addition to GpSGHV, tsetse flies also harbor symbiotic bacteria—hologies ,10,11,12Iflaviridae family and the newly described Negevirus taxon [Iflaviridae family comprises positive-sense single-stranded RNA (+ssRNA) viruses in the order Picornavirales and contains a single genus, Iflavirus. The Iflavirus ssRNA genome encodes a single, large polyprotein, which is post-translationally processed into structural and non-structural viral proteins essential for its replication, packaging and transmission [Using next-generation sequencing (NGS) approaches, multiple insect infecting viruses have been characterized in other insects, like mosquitoes and sand-flies, such as those viruses belonging to the us taxon ,14. The smission . The sinsmission ,16.Negevirus taxon are enveloped and have a +ssRNA genome of about 9 to 10 kb with a poly(A) tail at the 3′ end and encodes three major open reading frames (ORFs). The largest Negevirus ORF (ORF1) is approximately 7 kb and encodes the viral non-structural proteins: ribosomal RNA (rRNA) methyltransferase (MTase), viral MTase, helicase and the RNA-dependent RNA polymerase (RdRp). ORFs 2 and 3 are approximately 1.5 kb and 600 base pairs (bp), and they have been hypothesized to encode structural proteins, the glycoprotein and membrane protein, respectively [Negevirus taxon, the Nelorpivirus and the Sandewavirus, with the former being more closely related to the plant cileviruses and higreviruses [Viruses in the newly described ectively . Two majeviruses . Howevereviruses .Iflaviridae family and Negevirus genus, respectively, both isolated from Glossina morsitans morsitans reared at the Insect Pest Control Laboratory in Seibersdorf, Austria. The tissue tropism of these viruses was analyzed to provide insights into the possible transmission mode. RT-qPCR and Stellaris RNA in situ hybridization techniques were applied to detect the viruses in the various organs.In tsetse flies, further investigations are required to provide crucial information on presence of persistent +ssRNA virus infections and how these might interact with the above-mentioned tsetse symbionts and GpSGHV. Here, we report the nucleotide sequences, genome organization and phylogenetic placement of two novel +ssRNA viruses belonging to G. m. morsitans) used in this study were obtained from the colony maintained at the Joint FAO/IAEA Center, Insect Pest Control Laboratory (IPCL), Seibersdorf, Austria. The flies were maintained in an insectary at 23 ± 1 °C, 75–80% relative humidity, with a 12 h photo-phase and were fed defibrinated bovine blood for 10 to 15 min, three times per week using an in vitro membrane feeding system.Tsetse flies (G. m. morsitans and produced using the Illumina HiSeq 1000 was downloaded from the NCBI database. The assembly and mapping of these data were performed on CLC Genomic WorkBench (GWB) version 11.0.1 to generate contigs. The contigs that mapped to other related viruses were used to design the initial primers to detect these viruses in G. m. morsitans samples from IPCL. A series of forward and reverse primers were designed to join the non-overlapping contigs that mapped to the same virus family. The 5′ and 3′ ends of each virus were amplified by rapid amplification of cDNA ends (RACE) using the 5′/3′ RACE Kit, 2nd Generation  in combination with virus-specific primers. Sequences and positions of the primers for the specific viral genomes used in this study are described in RNA-Seq data (SRR965341) derived from a pool of 30 salivary glands of trypanosome-infected https://www.ncbi.nlm.nih.gov/orffinder/, accessed on 15 October 2018) to search for ORFs, while the amino acid sequences were used to scan for conserved functional domains and putative proteolytic processing sites using InterProScan  and HMMER programs . The secondary structures of the 5′ UTR for both virus genomes were predicted using MFOLD program and ViennaRNA software package [The assembled virus genome sequences were characterized based on their size, organization, and the encoded proteins. Briefly, the nucleotide sequences for both genomes were subjected to ORF finder  and the oligo-dT primer included in the kit following the manufacturer’s instructions.Total RNA was extracted from 10 individual whole bodies  of 3–4 days old flies using TRIzol reagent  according to the manufacturer’s instructions. The extracted RNA samples were treated with DNase 1  to remove any contaminating genomic DNA, and the RNA purity and concentration were determined using a Nanodrop ND-1000 spectrophotometer . A total of 500 ng of RNA was reverse transcribed to complementary DNAs (cDNAs) using the SuperScriptG. m. morsitans fly samples, one primer pair  and sequenced by Sanger sequencing technology. Multiple alignments of the identified sequences were performed in BioEdit  as well To detect the replicative negative RNA strand of the identified viruses, cDNA was synthesized using a forward virus-specific primer (complementary to the negative RNA strand) from a DNase-treated total RNA. To confirm that the cDNA was generated from the virus negative strand, a PCR was performed using a primer pair located downstream of the forward virus-specific primer used at the reverse transcriptase (RT)-step. For each of the analyzed samples, a negative control without RT enzyme (NRT), a positive control (cDNA synthesized from the virus positive RNA strand) and no-template (RNase-free water as template) samples were included in this assay. The primers used are detailed in G. m. morsitans, quantitative real time PCR (RT-qPCR) and the Stellaris RNA-Fluorescence in situ Hybridization (FISH) techniques were applied for detection and visualization of the viruses, respectively, in different tsetse isolated tissues. To isolate the tissues, 30-day-old flies were starved for 2 to 3 days then chilled at 4 °C for 15 min prior to dissection and were kept on ice during the dissection of different tissues. The tissues  were dissected in sterile phosphate-buffered saline (1X PBS) under a dissection microscope. Tissues for RT-qPCR analysis were collected in microtubes containing RNA later, while those for FISH were collected in fixating solution  and were kept on ice during the dissection process. For the RT-qPCR analysis, the same tissues from 10 adult flies were pooled together and considered as one replicate. Male and female tissues were separated, with a total of three replicates for each sex.To determine the tissues infected with GmmIV and GmmNegeV in adult tsetse flies 2O and 7.5 μL iQTM SYBER Green Supermix . Three technical replicates per virus target and housekeeping (β-tubulin) gene were performed. The PCR was performed in a CFX96 PCR machine , with an initial denaturation step at 95 °C for 2 min, followed by 39 cycles of 95 °C for 5 s, 62 °C for 30 s, ended by a fluorescence read and 95 °C for 5 s, followed by a melting curve from 65 to 95 °C. The real-time data were analyzed using the 2(−ΔΔct) method using the CFX Maestro software version 2 (Bio-Rad).To detect and quantify GmmIV and GmmNegeV from the dissected tissues, total RNA was extracted and cDNA was synthesized as described in ® FISH probe designer  according to a protocol previously described by Schneider et al. [Wolbachia were also analyzed using W1 and W2 probes targeting 16S rRNA [® RNA-probe and 3 pmol of W1 and W2 Wolbachia probes. Following hybridization, the tissues were briefly washed 3 times with preheated washing buffer at room temperature followed by one wash for 30 min at 37 °C in the dark hybridization chamber. Tissues were then incubated with Alexa Fluor® 488 phalloidin  staining F-actin (1:100 in 1X PBS) for 1 h, shaking at room temperature in the dark. Alexa Fluor® 488 phalloidin was then replaced by 1X PBS, and the tissues were mounted in Roti-Mount© FluorCare , containing DAPI  on sterile slides. Visualization of the hybridized tissues was performed on either a Nikon A1 or an Olympus FluoView 3000 confocal microscope. G. pallidipes was used as a negative control because the two viruses investigated here have remained undetectable in this species by RT-PCR.To visualize the two viruses in adult tsetse flies using FISH technique, custom probe sets for GmmIV and GmmNegeV were designed using the Stellarisr et al. . The end16S rRNA  of the bβ-tubulin expression.RT-qPCR data were analyzed with the general linear model (GLM) on R v 4.0.5  using RSG. m. morsitans flies. The assembly of the RNA-seq data in CLC GWB generated 6843 contigs containing >500 reads. Six contigs mapped to two positive single-stranded RNA (+ssRNA) viruses. Using BLASTx, two non-overlapping contigs (#1 and 83) mapped to a non-annotated Kinkell iflavirus sequence (AMO03216.1) with 43 and 45% sequence identity, respectively, while four other non-overlapping contigs  mapped to Negeviruses, a newly identified insect-infecting virus group. Contigs #1351 and 1602 showed low levels (23–61%) of amino-acid identity with Dezidougou virus (JQ675604.1) and Tanay virus (KF425262.1), and contigs #215 and 2539 showed 47% and 36% of amino-acid identity with Tanay virus (KF425262.1). To confirm the presence of the two viruses, the initial RT-PCR was performed on the G. m. morsitans samples using primer sets located in each of the identified contigs and sequenced (The sequences used for the discovery of the RNA viruses in tsetse originated from the RNA-seq data of both trypanosome infected and non-infected equenced . RT-PCR equenced . TherefoThe assembled GmmIV genome sequence contains 9685 nucleotides (nts) with a single, large ORF (9222 nts) encoding a polyprotein that is cleaved into both structural proteins  and non-structural proteins. The viral genome contains a single ORF, which is flanked by a 5′ UTR (300 nts) and a 3′ UTR 163 nts) that ends in a poly(A) tail A. The en3 nts thaG. m. morsitans is composed of 8140 nts encoding two overlapping ORFs flanked by 5′ UTR and 3′ UTR. The GmmNegeV ORF1 (7035 nts) encodes a polyprotein containing four typical non-structural protein domains: a methyltransferase (MT) domain, a ribosomal RNA methyltransferase domain, a viral RNA helicase domain and an RdRp domain  that are associated with the viral envelope.The GmmNegeV genome isolated from p domain C. The sePicornavirales in GenBank. The RdRp segregated the viruses into taxonomic groups  within the order Picornavirales and clustered GmmIV with viruses belonging to the Iflaviridae family such as Wuhan fly virus 4  was used as template. The RT-PCR products from both positive and negative RNA templates were sequenced to confirm the sequences of both viruses.During replication of +ssRNA viruses, a complementary negative RNA strand is normally transcribed from the positive RNA genome, which serves as the template for multiplication of the viral genome to form new virus progeny. To provide evidence that both GmmIV and GmmNegeV replicate in tsetse flies, a strand-specific RT-PCR was performed using cDNA transcribed from either positive and negative RNA strands of the two viruses a,b. A pr2 = 28.65, df = 9, p = 0.0007; GmmNegeV: X2 = 29.21, df = 9, p < 0.01). In particular, GmmIV density varied in male fly tissues  with the highest density in testes and midguts and the lowest detected in the hind gut, while in female fly tissues , the highest GmmIV density was observed in fat bodies and the lowest in the ovaries  and female  tissues, with the highest density in the fat bodies and salivary glands and the lowest in hind gut and Malpighian tubes in both sexes . To date, Negeviruses have only been associated with mosquitoes and sandflies isolated in a number of geographic locations, including Europe, United States, Indonesia and Africa ,19. LikeSandewavirus group are Tanay virus, Santana virus, Bustos virus, Dezidougou virus and Goutanap virus while those belonging to the nelorpiviruses are Negewotan virus, Brejeira virus, Piura virus and Loreto virus [Phylogenetic analysis of the insect-specific negeviruses classify them into two clades; nelorpiviruses and sandewaviruses, which suggests two separate origins of the viruses belonging to these two groups, with nelorpiviruses being more closely related to plant viruses (cileviruses and higreviruses) . In thisto virus ,19. Due to virus ,38. Thisto virus .G. m. morsitans, as clearly confirmed by detection of the replicative negative strand RNA of GmmIV and GmmNegeV. Furthermore, we investigated tissue tropism of these viruses in G. m. morsitans as a model. Until now, Iflaviruses and Negeviruses were only known to be present in mosquitoes, sandflies and beet armyworm [Our study has provided compelling evidence that GmmIV and GmmNegeV replicate in armyworm ,40. NothG. pallidipes [Sodalis [in situ technique, but both viruses were detected by RT-qPCR. As such, the viruses may only be present in the saliva itself but remain undetectable in the epithelial cells by the in situ technique. Another reason for the low presence of the viruses in the salivary glands might be that during low rearing densities of tsetse flies, vertical transmission might be favored. However, further experiments are needed to understand the impact of different tsetse rearing densities on these viruses’ horizontal transmission. In addition, all the experiments in this study were performed on colony flies and therefore, data on the possible presence of these two newly described viruses in natural glossinidae fly populations would be crucial as the virus dynamics in the wild may be different as the environment changes drastically.A vertical transmission has already been demonstrated in the case of GpSGHV in llidipes , which m[Sodalis . In our [Sodalis , who sho[Sodalis . In our G. m. morsitans flies analyzed in this study showed any overt virus infections. Notably, G. pallidipes was the only species at the IPCL that both GmmIV and GmmNegeV were undetectable, and it is the most susceptible species for GpSGHV overt infections [In tsetse rearing facilities, it is unclear if the presence of GmmIV and GmmNegeV has a beneficial or a detrimental effect on the flies. As reported for most Iflaviruses and all Negeviruses , none offections . This mafections . It is tIn conclusion, this study expands the list of viruses infecting tsetse flies in addition to GpSGHV as well the symbiotic bacteria harbored by tsetse flies. However, the host range and better understanding of these virus−symbionts−host interactions as well as their effects on tsetse vector competence to trypanosome require further investigation.


Viruses pose a challenge to our imaginations. They exert a highly visible influence on the world in which we live, but operate at scales we cannot directly perceive and without a clear separation between their own biology and that of their hosts. Communication about viruses is therefore typically grounded in mental images of virus particles. Virus particles, as the infectious stage of the viral replication cycle, can be used to explain many directly observable properties of transmission, infection and immunity. In addition, their often striking beauty can stimulate further interest in virology. The structures of some virus particles have been determined experimentally in great detail, but for many important viruses a detailed description of the virus particle is lacking. This can be because they are challenging to describe with a single experimental method, or simply because of a lack of data. In these cases, methods from medical illustration can be applied to produce detailed visualisations of virus particles which integrate information from multiple sources. Here, we demonstrate how this approach was used to visualise the highly variable virus particles of influenza A viruses and, in the early months of the COVID-19 pandemic, the virus particles of the then newly characterised and poorly described SARS-CoV-2. We show how constructing integrative illustrations of virus particles can challenge our thinking about the biology of viruses, as well as providing tools for science communication, and we provide a set of science communication resources to help visualise two viruses whose effects are extremely apparent to all of us. Viruses compel our attention and frustrate our imagination. The impact of pathogenic viruses on everyday life is all too easy to observe, but viruses themselves are not a part of the visible world. This is partly because, like all microorganisms, they operate on a scale too small for the unaided eye to see, but an even greater challenge in forming a clear image of viruses is the intimacy with which they parasitise their hosts. Unlike parasitic cellular microorganisms such as bacteria or eukaryotic microbes, viruses are not discrete bodies that draw nutrition from their hosts . InsteadIt is often helpful, both for the wider public and for virologists themselves, if the term ‘virus’ can be grounded in a more concrete mental image. To do this, we conventionally turn to the one part of the virus replication cycle that has a discrete and distinctive physical form, and which clearly separates viruses from other symbiotic nucleic acids such as plasmids or transposons . When thVirus particles are only one step in the virus replication cycle, and for many viruses they are absent during most stages of viral replication. Nonetheless, the image of a virus particle is helpful when thinking about viruses. As noted above, the production of virus particles is one of the characteristic features of viruses, distinguishing them from other symbiotic nucleic acids . The sizThe first challenge in visualising a virus particle is that the question ‘what does a virus particle look like?’ does not actually have a direct answer. Virus particles are typically smaller than the wavelength of visible light, undergo constant thermal motion at speeds our brains are not well-equipped to deal with, and are small enough that their shape is defined by the electrostatic properties of individual molecules. There are, however, well-established conventions for simplifying these issues. Most people can interpret a visual model that answers the alternative question ‘what would a virus particle look like if it was enormously bigger in size, moved very slowly if at all, and was made of components with clearly-defined edges?’ To address this simpler question we still have the challenge of defining the shapes of a virus particle’s constituent macromolecules and how they relate to each other. There are currently two ways to do this.The most direct approach to visualise a virus particle is from experimental measurements. One of the striking features of many virus particles, particularly those of non-enveloped viruses, is their strict regularity. This has been exploited by structural biologists, who can use the large numbers of identical virus particles present on electron microscopy grids or, implicitly, in diffracting crystals to generate high-resolution average structures. Such structures are strikingly beautiful and often contain enough structural information to build testable models of viruses as molecular machines .However, direct experimental measurement is often not sufficient. Many viruses, particularly those bound by a lipid membrane, are pleiomorphic, or continuously variable in form. A population of such particles will show considerable variation in their dimensions and proportions, and no two virus particles will be entirely identical – the ideal, average, virus particle may or may not even exist. In such cases, experimentally averaged structures provide limited detail. Instead, a model of the virus particle has to be obtained by integrating data from multiple sources. This is typically done by fitting the structures of individual molecules, determined experimentally or predicted by modelling, into low-resolution electron micrographs or tomograms of individual virus particles.The synthesis of diverse data sources to understand complex biological structures that are inaccessible by any one method is the subject of the emerging field of integrative structural biology . IntegraHowever, the application of rigorous integrative methods to variable virus particles is not trivial. Integrative modelling of virus particles requires rich datasets: to identify and quantify the proteins encoded by both the virus and its host that are packaged into virus particles; to precisely define their individual and higher-order structures; and to map their interactions and the organisation of the virus particle as a whole. Often this information is not sufficiently complete for a rigorous mesoscale model to be produced. The vast majority of viruses are not yet studied in sufficient detail . Some viIn such cases, the ‘visualisation gap’ between individual molecular structures and overall virion morphology can be tackled using artistic approaches informed by scientific data. Scientific illustration has long been effectively applied to virology, both to communicate established models of viruses effectively to a wider audience and to challenge scientific thinking about the way viruses function . We receInfluenza viruses are major human and veterinary pathogens . In humaOne challenge is that influenza viruses, as studied in the laboratory, are rather more regular than those that occur in natural infections. Laboratory-adapted strains of influenza virus typically form spherical or bacilliform virions of around 120 nm diameter, but low-passage clinical isolates can form additional, filamentous virus particles that are slightly narrower and can extend for microns in length, reaching tens to hundreds of times the length of the spherical particles . These fTo address this, we built models of intact influenza virus particles using Autodesk 3ds Max  particles grown in Madin Darby Bovine Kidney cells and purified on chicken erythrocytes . As thisWe had initially intended to create a detailed model of a spherical virus particle of 120 nm external diameter, a commonly reported form of laboratory-adapted virion and the shape most often used in illustrations of influenza virus particles . HoweverWith this information, we were able to construct a detailed three-dimensional model of an influenza virus particle, incorporating the structures of both virus and host proteins . ConstruIn response to the need for effective science communication in the early days of the SARS-CoV-2 pandemic, we sought to apply a similar literature-informed illustrative approach to constructing a model of the SARS-CoV-2 virus particle. In order to illustrate a virus particle whose basic features were, in March 2020, still being worked out, we drew on literature describing the dimensions, stoichiometry and structural organisation of virus particles of the closely-related SARS-CoV-1 (SARS-CoV)  as well As more information is acquired about SARS-CoV-2, there is a growing potential for developing integrative models of its virus particles, as their features can be placed under detailed experimental constraints . HoweverThe intense interest in SARS-CoV-2 means that our model is now only one of a large number of visualisations showing the virus particle in different levels of detail . There aAn increased level of detail. For example, by including glycans, which can block antibody binding as well as regulating receptor binding interactions .The refinement of specific details. For example, the helical structure of the influenza RNP was initially solved with ambiguous handedness , and is Visualising diversity. Visualisations of virus particles tend to focus on a single idealised form, and the heterogeneity of virus particles produced by both influenza and SARS-CoV-2 virus particles is rarely shown.Visualising dynamics. Virus particles are typically visualised as static structures, but they undergo constant thermodynamic motion and specific conformational changes that are relevant to their function.Visualising context. While most visualisations of virus particles consider them in isolation, in reality they are part of, and interact with, a complex and crowded molecular environment. In the case of SARS-CoV-2 and influenza virus particles, even when airborne, this environment is typically respiratory mucus .However, it is important to recognise that the best level of detail and accuracy in any visualisation is not necessarily the greatest level possible. Good visualisations are a form of communication, and therefore include design choices that reflect the needs and expectations of their intended audience. When putting visualisations of virus particles to use, the needs of different audiences can vary considerably.As discussed above, the process of constructing integrative illustrations of the virus particles of influenza virus and SARS-CoV-2 was directly helpful to us as virologists. Doing so required us to synthesise ideas, challenged our preconceptions, and encouraged us to extend our own thinking about the molecular biology of these viruses. The primary purpose of this work, however, was to support science communication with others. In the time since the models have been constructed this has taken place in three distinct ways .The most straightforward use of the models, as for any visualisation, was decorative. As well as being visually appealing, such models provide an immediate indication that a resource relates to viruses. The style in which the virus particle is represented adds nuance to this – while simplified designs can be used to indicate widely accessible content, very detailed visualisations of virus particles convey credibility and can be used to highlight that a resource is aimed at engaged or expert audiences. Our visualisations were used in this way in the websites of virology departments, virology resources and PhD programmes, as well as being included in literature discussing the pandemic .Sketchfab, an online resource which allows three-dimensional structures to be explored in web browsers and in virtual reality, and which provides the potential for them to be embedded in virtual reality teaching resources  can be downloaded from the University of Glasgow’s Enlighten Data Repository at Click here for additional data file.


Case Presentation. A 73-year-old man presented with ataxia and difficulty in speech which began 3 months after initiation of treatment for idiopathic thrombocytopenic purpura with rituximab. Neurological examination was significant for torsional nystagmus, motor aphasia, right-sided dysmetria, and dysdiadochokinesia with gait ataxia. Magnetic resonance imaging (MRI) showed right cerebellar lesion and cerebrospinal fluid (CSF) polymerase chain reaction (PCR) was positive for JC virus.  John Cunningham virus (JCV) is known to cause progressive multifocal leukoencephalopathy (PML) in immuno-compromised patients due to lytic infection of oligodendrocytes and astrocytes. Rarely, it may also present as granule cell neuronopathy (GCN), leading to degeneration of cerebellar granule cell neurons. It is described in patients with underlying conditions or medication contributing to immune compromise.  The diagnosis of JC virus-related cerebellar disease can be missed, due to the subacute to chronic onset and challenges in detection. Clinicians should have a high degree of suspicion for development of these symptoms, even a few months after initiation of immune-modulatory therapy because the progression and outcomes can be disastrous. JC virus (JCV) is most commonly known to cause progressive multifocal leukoencephalopathy (PML) in immuno-compromised patients. Granule cell neuronopathy (GCN) is a rare manifestation of the JC virus that affects cerebellar granule cell neurons, caused by a mutation in the viral protein 1 (VP1) gene that results in changes in cell tropism [A 73-year-old man, who was diagnosed with non-Hodgkin's mantle cell lymphoma, in remission following chemotherapy in 1998, presented in September 2018 with motor aphasia, right upper limb dysmetria, and ataxia. He had a chronic history of idiopathic thrombocytopenic purpura, which was being treated with romiplostim infusions and oral corticosteroids. In June 2018, he developed worsening of idiopathic thrombocytopenic purpura (ITP) and was initiated on weekly rituximab. He received 8 infusions of rituximab. The neurologic exam was notable for torsional nystagmus, slurred speech with intact comprehension, dysmetria on right finger to nose test, and dysdiadochokinesia with gait ataxia. Cognition, strength, and sensation were intact. The biceps, brachioradialis, triceps, and patellar reflexes were 3/4 bilaterally and Achilles reflexes were 2/4 bilaterally. Plantar reflexes were flexor bilaterally.Magnetic resonance imaging (MRI) demonstrated a hyperintense lesion without mass effect in the right cerebellar hemisphere with no supra tentorial pathology . ExtensiMRI spectroscopy done in October 2018, showed elevation of choline peaks and depression of N-acetyl aspartate (NAA) peaks with mildly prominent lipid peaks, consistent with a cellular process. In November 2018, CSF analysis for JC virus PCR was positive, consistent with JC virus-related rhombencephalitis. Brain biopsy was deferred due to high risk of complications related to location of the lesion.In December 2018, he was referred to a tertiary care center. On evaluation, he was noted to have progression of lesions on MRI brain with confluent T2/FLAIR hyperintensities in the cerebellar hemispheres, pons, and midbrain compared to the previous scan from November 2018. JC virus PCR in CSF showed 409 copies and JC virus PCR in plasma showed 3200 copies per mL. He was enrolled on a protocol for a phase II study assessing the effect of BK specific cytotoxic T lymphocyte lines (CTLs) generated by ex vivo expansion in patients with BK virus infection and JC virus infection. He received his first infusion of BK specific CTLs in December 2018, to which he had a partial response. Repeat MRI showed decreased patchy enhancement with stable extent of confluent T2 FLAIR signal changes in the cerebellum and pons, suggestive of positive treatment response of progressive multifocal leukoencephalopathy. Repeat testing revealed less than 72 copies per ml of JC virus in CSF and 2100 copies per mL of JC virus in plasma. He tested negative for BK virus in urine and plasma. Second BK specific CTL infusion was given in January 2019.The patient had multiple emergency room visits and hospital admissions for influenza A and urinary tract infections. He developed progressive weakness and falls and required inpatient rehabilitation to recuperate. Repeat MRI showed increased confluent T2/FLAIR hyperintensities in the cerebellar hemispheres, pons, and midbrain compared to the previous scan. Repeat quantitative JC virus levels in CSF showed an increase (267 copies per mL) compared to prior value, JC virus in plasma was 2000 copies per mL. Given his rapid decline, the family opted to invoke hospice care and was taken off the study in late February 2019.Polyomavirus JC (JCV) has a sero-prevalence of 50–86% among healthy adults, but remains dormant in immunocompetent persons. Conditions of immunosuppression such as AIDS , organ tGCN is shown to exhibit cell tropism. A study of four patients and review of literature of ten cases described variable changes in VP1 structures and a JCV GCN variant of the virus . GCN canIn a review of literature looking at PML after rituximab, median time from the last rituximab dose to PML diagnosis was 5.5 months. Median time to death after PML diagnosis was 2.0 months. The case-fatality rate was 90% . A case The PCR is a fast, reliable, and conveniently performed assay. In one study done in 84 HIV negative patients, JC virus was detected by a nested PCR in 6 patients (7%), contributing to confirmation of PML in the background of immunosuppressive conditions. The specificity of the PCR was confirmed by a clinical follow-up study which showed full agreement between the detection of JCV DNA in CSF and clinically manifested PML . The senMost conventional DNA PCR tests have been shown to have a cut-off of >50 copies per mL. The sensitivity of the test is not well established because the prevalence of the disease is low. It is important to maintain a high index of clinical suspicion in cases with progressive CNS degeneration with underlying immunosuppression, despite negative PCR. In such cases, ultrasensitive PCR can be used to detect JC virus as it has a lower threshold of >10 DNA copies per mL.Brain biopsy is the gold standard for diagnosis of GCN, but high risk location of the lesion and hematological contraindications may limit feasibility. On imaging, GCN may present with heterogeneous or nonspecific features. A case series and review of literature described the most common feature to be cerebellar atrophy, which often occurs in combination with white matter changes in the cerebellum and brainstem . The typThere are no clear studies descriptive of the natural history of the disease. Some case reports have described improvement and stalling of disease progression after stopping the offending agent and recovery of the immune system . Other rCausation cannot be clearly established between chemotherapy and JC virus leading to GCN in view of underlying lymphoma, but the onset of symptoms in the case described is temporally associated with initiation of rituximab. The clinical presentation can be confounding, with vague symptoms and subacute to chronic onset and noncontributory imaging and CSF findings. So, clinical vigilance to anticipate complications is important while dealing with these patients. Further research is needed to quantify risk of developing PML in patients being initiated on immunosuppressive therapy.


Morphospaces—representations of phenotypic characteristics—are often populated unevenly, leaving large parts unoccupied. Such patterns are typically ascribed to contingency, or else to natural selection disfavoring certain parts of the morphospace. The extent to which developmental bias, the tendency of certain phenotypes to preferentially appear as potential variation, also explains these patterns is hotly debated. Here we demonstrate quantitatively that developmental bias is the primary explanation for the occupation of the morphospace of RNA secondary structure (SS) shapes. Upon random mutations, some RNA SS shapes (the frequent ones) are much more likely to appear than others. By using the RNAshapes method to define coarse-grained SS classes, we can directly compare the frequencies that noncoding RNA SS shapes appear in the RNAcentral database to frequencies obtained upon a random sampling of sequences. We show that: 1) only the most frequent structures appear in nature; the vast majority of possible structures in the morphospace have not yet been explored; 2) remarkably small numbers of random sequences are needed to produce all the RNA SS shapes found in nature so far; and 3) perhaps most surprisingly, the natural frequencies are accurately predicted, over several orders of magnitude in variation, by the likelihood that structures appear upon a uniform random sampling of sequences. The ultimate cause of these patterns is not natural selection, but rather a strong phenotype bias in the RNA genotype–phenotype map, a type of developmental bias or “findability constraint,” which limits evolutionary dynamics to a hugely reduced subset of structures that are easy to “find.” As Stephen J. Gould wrote in a critique of those who make this implicit assumption  maps that are tractable enough to provide access to the full spectrum of possible variation  so that RNA is a versatile molecule. Made of a sequence of four different nucleotides (AUCG) it can both encode information as messenger RNA (mRNA), or play myriad functional roles as ncRNA . This abThe function of ncRNA is intimately linked to the three-dimensional (3D) structure that linear RNA strands fold into. Although much effort has gone into the sequence to 3D structure problem for RNA, it has proven to be stubbornly recalcitrant to an efficient solution . By contAn important driver of the growing interest in GP maps is that they allow us to open up the black box of variation—to explain, via a stripped-down version of the process of development, how changes in genotypes are translated into changes in phenotypes. Unfortunately, it remains much harder to establish how the patterns typically observed in studies of GP maps  translatG-sampling (L = 30 to L = 50), they found that natural ncRNA from the fRNAdb database –(C) above can be found in one of the most striking properties of the RNA SS GP map, namely strong phenotype bias which manifests in the enormous differences in the G-sampled frequencies  of the SS . For exaerformed , the difagnitude . Such phagnitude : a smallEvolutionary modeling that takes strong bias in the arrival of variation into account is rare. Population-genetic models that do include new mutations typically consider a genotype-to-fitness map, which often includes an implicit assumption that all phenotypes are equally likely to appear as potential variation, something akin to P-sampling. A notable exception is a work by p appears in a population made up of phenotype q can be quite accurately approximated as qρ is the mean mutational robustness of genotypes mapping to q. In other words, the average local rate at which variation p appears in an evolving population closely tracks the global frequency For the specific case of RNA, however, the effect of strong phenotype bias was treated explicitly in arrival of the frequent, where the enormous differences in the rate at which variation arrives implies that frequent phenotypes are likely to fix, even if other higher fitness, but much lower frequency phenotypes are possible in principle. This same effect has also been observed in the evolutionary modeling of gene regulatory networks . One way of thinking about DNNs is as mappings from the (adjustable) parameters of the DNN (the genotypes), to functions (the phenotypes), which describe how input data maps to a DNN output. The volume of parameters mapping to a particular function is directly proportional to the probability that this function obtains upon random sampling of parameters, and is analagous to the NSS in GP maps. As was found for the RNA GP maps, the mapping from parameters to DNN functions can be hugely biased .Of course, DNNs are not trained by randomly sampling parameters, just as an evolutionary process does not use G-sampling either. Instead, the most popular way to optimize DNNs is by using stochastic gradient descent (SGD)  which foIt is interesting to consider whether our arguments that strong phenotype bias affects adaptive evolution can shed light on a related controversy around mutational biases. For example, pf and Mutation bias is also typically much smaller than phenotype bias . The gloariation . Interesnal bias , may indfunction emerges , we hypothesize that the same structures emerge because of phenotype bias. After all, multiple structures could, in principle, produce the same function. In other words, to use Mayr’s famous ultimate-proximate distinction  and the Bioconda rnashapes package available at https://anaconda.org/bioconda/rnashapes (last accessed August 2021). The option to allow single-bonded pairs was selected, to accommodate the Vienna folded structures which can contain these.RNA SS can be abstracted in standard dot-bracket notation, where brackets denote bonds, and dots denote unbonded pairs. To obtain coarse-grained abstract shapes  of diffeFor each length, we took all available natural noncoding RNA sequences from the RNAcentral database . Any repeated sequences were discarded. The sequence secondary structure predictions were made by the Vienna package, and then abstract RNA shapes for each structure were obtained.L = 40: 3,160 sequences, yielding 18 unique shapes at level 3 (18/18 shapes were found by random sampling);L = 55: 8,619 sequences, yielding 63 unique shapes at level 3 (63/63 found);L = 70: 78,075 sequences, yielding 16 unique shapes at level 5 (16/16 found);L = 85: 76,143 sequences, yielding 25 unique shapes at level 5 (24/25 found);L = 100: 20,314 sequences, yielding 35 unique shapes at level 5 (35/35 found);L = 126: 14,318 sequences, yielding 68 unique shapes at level 5 (68/68 found).The numbers of natural sequences, numbers of shapes, and fractions of natural shapes found by random sampling, were:In total 224/225  of the shapes in the database were found by relatively modest sampling.Molecular Biology and Evolution online. msab280_Supplementary_DataClick here for additional data file.


Micaelamys namaquensis). We characterised four complete virus genomes, which showed typical mammarenavirus organisation. Phylogenetic and genetic distance analyses revealed: (i) the presence of a significantly divergent strain of Luna virus in Angolan representatives of the ubiquitous Natal multimammate mouse , (ii) a novel Okahandja-related virus associated with the Angolan lineage of Micaelamys namaquensis for which we propose the name Bitu virus (BITV) and (iii) the occurrence of a novel Mobala-like mammarenavirus in the grey-bellied pygmy mouse (Mus triton) for which we propose the name Kwanza virus (KWAV). This high virus diversity in a limited host sample size and in a relatively small geographical area supports the idea that Angola is a hotspot for mammarenavirus diversity.Rodents are a speciose group of mammals with strong zoonotic potential. Some parts of Africa are still underexplored for the occurrence of rodent-borne pathogens, despite this high potential. Angola is at the convergence of three major biogeographical regions of sub-Saharan Africa, each harbouring a specific rodent community. This rodent-rich area is, therefore, strategic for studying the diversity and evolution of rodent-borne viruses. In this study we examined 290 small mammals, almost all rodents, for the presence of mammarenavirus and hantavirus RNA. While no hantavirus was detected, we found three rodent species positive for distinct mammarenaviruses with a particularly high prevalence in Namaqua rock rats ( Arenaviridae) have recently been reported in a wide range of vertebrates, e.g., frogfishes, salmon and viperid snakes [Mammarenavirus. Based on antigenic properties, geographical distribution and phylogenetic relationships, members of the genus are further divided into Old World (OW) and New World (NW) groups comprising 19 and 20 distinct mammarenavirus species, respectively [Although arenaviruses , has been described in Gabon [Mastomys natalensis, are common in this part of Africa, no additional cases have been reported. This trend is particularly true for Angola, one of the most understudied regions concerning rodent diversity in Africa [An increasing number of studies have investigated the occurrence of mammarenaviruses in small African mammals, revealing a rich diversity of viruses in rodent hosts. These studies were undertaken in West Africa  and Mariental virus (MRLV) in Namibia [Grammomys sp. is the carrier of Solwezi virus (SOLV) [Cytochrome b sequence of the Grammomys host associated with the SOLV (GenBank AB972437), it shows 99% identity with Grammomys surdaster lineage su7  the Congolian region characterised by moist forests and savannahs; (2) the Zambezian region characterised by woodlands, savannahs and floodplains and (3) the South African region characterised by arid savannahs, dwarf shrublands and desert . This, to et al. ) is knowo et al. ,21 and Mo et al. ,23; for  Namibia ; and fins (SOLV) . If we ia et al. ), which á et al. . Angola,Hantaviridae. This group of viruses was only identified in Africa in 2006 [Hylomyscus simus in Guinea, the majority of hantaviruses found in Africa are from bats [Stenocephalemys albipes and S. zimai in Ethiopia [Rattus rattus) in Senegal [Another group of rodent-borne viruses, well known as human pathogens, is the family  in 2006 , though  in 2006 . The resrom bats ,29,30,31rom bats ,33,34,35Ethiopia ,36, and  Senegal . FurtherIn this study we investigated if the rich community of Angolan rodents was reflected in the diversity of rodent-borne viruses. For this, we screened the samples collected by Krásová et al.  during a® filter papers , and kidney samples in RNAlater stabilisation reagent  which were kept at −80 °C for long-term storage.In July 2017, a total of 290 small mammals representing 17 genera, and essentially composed of rodents, were captured across varied biotopes of south-western Angola . The traCytochrome b gene sequencing [M. triton were published in a previous evolutionary survey [Cytochrome b gene of the other 19 mammarenavirus-positive individuals as previously described. Host Cytochrome b sequences were deposited in GenBank under accession numbers MZ065484-MZ065502 (this study), MW544578-9 and MW544581 [M. triton sequences are designated MK011523 and MK011526 [Field morphological identification of the rodent species was verified by quencing . Five ofquencing , of whicy survey . In thisMW544581 . M. tritMK011526 .® Reverse Transcriptase  and random hexamers. Samples were then tested for the presence of viruses by PCR assay targeting the L gene for arenaviral polymerase [L gene for hantaviral polymerase [NP gene for arenaviral nucleoprotein and the GPC gene for glycoprotein precursor [Small pieces of kidney were merged into pools by species and locality, two or a maximum of three individuals per sample, followed by viral RNA extraction with the NucleoSpin RNA Kit . Complementary DNA (cDNA) synthesis was performed according to the manufacturer’s instructions using the Maximalymerase  using thlymerase  using threcursor  using Phrecursor  to estimrecursor .M. natalensis, ANG0052 and ANG0070 from M. namaquensis and ANG0206 from M. triton for metagenomics. For ANG0070 and ANG0117, we used the RNA previously extracted from kidney tissue. For the two other samples, we extracted RNA from dried blood (ANG0052) and dried saliva (ANG0206) using QIAamp Viral RNA Mini Kit . For ANG0206, cDNA synthesis, library preparation and sequencing followed the procedures described in Goüy de Bellocq et al. [http://www.cbs.dtu.dk/services/NetNGlyc/ accessed on 10 January 2021).We selected four samples positive for mammarenaviruses, ANG0117 from q et al. . For theq et al.  and trimq et al. . De novoq et al. . Assemblq et al. . When coGPC gene). Nucleotide viral coding regions of the GPC, NP and L gene were aligned based on translated sequences with a set of homologous sequences of OW mammarenaviruses using MUSCLE [Nannomys subgenus-specifically, mammarenaviruses from Mus minutoides, Mus mattheyi, Mus setulosus and Mus baoulei). For MRLV described in Micaelamys namaquensis in Namibia, the sequence of the L segment available in GenBank (KP867641) shows a fragment of ~870 nt that does not align with other L mammarenavirus sequences and instead shows 98% sequence identity with the MRLV S segment (KM272987). The KP867641 L segment sequence appears to be a chimeric assembly, the first ~4680 nt belonging to the L segment, the next ~870 to the S segment and the last ~1290 nt to the L segment. In the paper describing this virus [L gene corresponding to the portion before the chimeric assembly was used in our phylogenetic analysis.Mammarenavirus nucleotide (nt) sequences obtained from the molecular screening were visually inspected in Geneious and manually trimmed for primers or when their quality was low  on the CIPRES web platform  using thn MEGA X .We examined 290 small mammals: nine sengis, one shrew and 280 rodents  for the presence of RNA of mammarenaviruses and hantaviruses. All samples tested negative for hantaviruses. In contrast, 24 samples were found positive for mammarenavirus RNA corresponding to a total prevalence of 8.3% (95% CI: 5.4–12%) .Mastomys natalensis, represented 32.4% of the sampled animals. In this rodent species, only one individual out of 94  was found to carry a mammarenavirus. BLAST and a preliminary phylogenetic analysis suggested this was a strain of Luna virus (LUAV). We captured this specimen (ANG0117) in Bicuar National Park (Huíla Province) with eight other M. natalensis, corresponding to 11.1% (95% CI: 0.6–44.4%) estimated prevalence at the studied locality . Specifically, we found 15 positive M. namaquensis in Tundavala  and 6 in Bibala  . BLAST and a preliminary phylogenetic analysis of the sequences revealed the presence of a putative novel mammarenavirus, to which we tentatively give the composite name Bitu virus (BITV), after the initial letters of Bibala (Namibe Province) and Tundavala (Huíla Province) localities.Among 75 individuals of 6.5–80%) . The preMus triton trapped 20 km SW of Cassongue town  were positive , ANG0052 and ANG0070 (Bitu virus) and ANG0206 (Kwanza virus), apart from six noncoding nt at the 3′ end of the L segment of KWAV. Z and L ORFs. The Z ORFs of the four viruses vary between 282–300 nt long and encode 93–99 amino-acid (aa) long zinc finger proteins. The Z proteins contain the regular motifs of OW mammarenaviruses  except BITV, which shows a shorter C-terminal domain. It is not possible to recognise any known late domains in this virus but, instead, a PTCP sequence that partially mimics the late domain PT/SAP found in most other OW mammarenaviruses. This pattern was already reported for Merino Walk virus [L ORFs of the viruses vary between 6660 and 6678 nt and encode 2219–2225 aa long RNA-dependent RNA polymerases. In the L protein, the canonical polymerase domains  and the key active site residues of the endonuclease domain NL1 [The complete L segments are 7187–7296 nt long and contain the two usual lk virus  and it imain NL1  are wellGPC and NP ORFs. The NP ORFs of the four viruses, 1689–1710 nt long and encoding 562–569 aa long nucleoproteins, contain aa motifs that resemble other OW mammarenaviruses, including a cytotoxic T-lymphocyte (CTL) epitope GVYMGNL described in LCMV [GPC ORFs of our viruses, 1470–1506 nt long and encoding 489–501 aa long glycoprotein precursors, show also usual OW mammarenavirus motifs. The motif at the GP1/GP2 cleavage site of the GPC protein is RRLL for Kwanza, RRLR for Bitu and RRLM for Luna virus. Four to six and three to four N-glycosylation sites can be detected in GP1 and GP2, respectively, in positions analogous to other African mammarenaviruses [The complete S segments vary between 3373 and 3476 nt long and contain the two usual  in LCMV . Howeveraviruses .Angolan LUAV shows two characteristics that are unique for this virus. First, the intergenic region of the S segment is large, 164 nt long, compared to other mammarenaviruses for which this region is 60–70 nt-long. A few other mammarenaviruses also have much larger IGRs than average  but LUAV has the largest S-segment IGR described so far. The second unique feature is the presence of a motif of 6 nt AAAATT repeated 10 times in the 3′ noncoding region of its L segment. These two features are absent in all other LUAV genomes available in GenBank.L gene and 1 in the noncoding 5′end of the L segment (see L gene implied nonsynonymous substitutions occurring at sites showing polymorphism among OW mammarenaviruses. The remaining eight polymorphic sites implied synonymous substitutions (KWAV shows 16 polymorphic nucleotide sites (neither base occurring at a frequency >75%), 15 located in the itutions . This suGPC, NP and L genes, Angolan LUAV (ANG0117) is basal to all previously identified LUAV strains from Zambia and Namibia, forming a highly supported monophyletic clade , except for LUAV SLW-1 with the pairwise identity of 88.4% , 12  of the NP gene  and eight  of the GPC gene . The phylogenetic position, similar for all three genes, grouped the viruses circulating in Angolan M. namaquensis with OKAV with high support  and NP  gene fragments of sample ANG0241. In the phylogeny, KWAV settled in a cluster of Mobala-like viruses, where it forms a sister lineage of MOBV , with various support of 0.76 to 1 depending on the gene (Z gene), 69.5% (L), 75.2% (GPC) and 76.7% (NP) identities at the nt sequence level and 62.6% (Z), 74.3% (L), and 88% (NP) identities at the aa sequence level. The maximum aa sequence identity of the GPC gene (85.7%) shares with Dhati Welel virus found in Mastomys natalensis in Ethiopia (Besides the whole genome of KWAV (ANG0206) , we obtathe gene . The PASthe gene . When caEthiopia . Thus, tM. namaquensis-borne virus. Finally, one of the new viruses, found in Mus triton, clusters in an unexpected part of the OW mammarenavirus phylogenetic tree. In contrast to the mammarenavirus results, no hantavirus was detected in the samples.In this study we detected three different mammarenaviruses in a limited sample size of rodents from a relatively restricted geographical area of Angola , and covHylomyscus heinrichorum could be another potential hantavirus reservoir. Indeed, Sangassou virus was not only found in H. simus in West Africa, but also more recently in H. endorobae in Kenya [Hylomyscus genus are distributed in lowland and montane rainforests of tropical Africa, where they can be locally very abundant. Local abundance is an important criterion that could promote the circulation of viruses [The absence of hantavirus detection in our samples supports the idea that rodents are not the most important reservoirs of these viruses in Africa. In fact, only two endemic hantaviruses have been confirmed to circulate in African rodents so far, Sangassou and Tigray ,26,36, ain Kenya . Wood mi viruses . LiteratM. namaquensis has recently been defined as a divergent phylogeographic taxon  from other species in the genus . Two of á et al. ), potent Namibia . For KWA viruses .M. namaquensis, which is particularly robust: 28% in this study compared with 1.13% reported for the related OKAV from Namibia [The relatively high overall prevalence of mammarenaviruses in this sample from Angola, reaching 8.3%, is essentially due to the prevalence of BITV in  Namibia . High loMurinae serve as hosts of all known African mammarenaviruses and likewise the genus Mus, subgenus Nannomys, carries several representatives. For instance, Lunk virus in Zambia, Kodoko in Guinea (both Mus minutoides) or Natorduori in Ghana (Mus mattheyi) [Mus musculus) worldwide  and Jirandogo (Mus baoulei) from West Africa, which cluster with the LASV complex or even represent a divergent strain of LASV [Praomyini tribe to members of the Murini tribe, subgenus Nannomys, but this does appear unlikely in three independent cases. Concerning KWAV, we did not find any arenavirus-positive individuals among three other Nannomys  and two Mastomys  subspecies/species captured at the same locality (Rodents of the subfamily attheyi) ,61,62. Torldwide  and whic of LASV ,63. One locality . So, it NP gene with LUAV SLW-1 slightly exceeds the ICTV criterion (88.4% at the aa level), we suggest it is rather a novel LUAV strain. In accordance with this, LUAV strain Bicuar groups with other LUAV strains into a monophyletic clade (M. natalensis from these three countries belong taxonomically to the same B-VI mitochondrial clade [M. natalensis from the bordering Democratic Republic of the Congo, belonging to the A-II clade, host a distinct mammarenavirus.For the Angolan LUAV, the genetic distance  and the ic clade . The preic clade . Moreoveal clade ,19, to wal clade ,19. It wIn summary, we identified Angola as a region of high mammarenavirus diversity, as predicted due to its rich rodent community. Our data support the current assumption about the complex evolutionary history of these viruses with strict host associations coupled with occasional host-switching events. The zoonotic potential of mammarenaviruses should not be underestimated either, as genetic plasticity can be an effective tool to facilitate spillover infection in these viruses . Almost


Environmental health services (EHS) in healthcare facilities (HCFs) are critical for providing a safe, functional healthcare environment, but little is known about their costs. Poor understanding of costs impedes progress towards universal access of EHS in HCFs. We developed frameworks of essential expenses required to provide EHS and conducted an ex-post financial analysis of EHS in a network of medical research and training facilities in Lilongwe, Malawi, serving an estimated 42,000 patients annually through seven outpatient buildings.We estimated the cost of providing the following EHS: water, sanitation, hygiene, personal protective equipment use at the point of care, waste management, cleaning, laundry, and vector control. We developed frameworks of essential outputs and inputs for each EHS through review of international guidelines and standards, which we used to identify expenses required for EHS delivery and evaluate the completeness of costs data in our case study. For costing, we use a mixed-methods approach, applying qualitative interviews to understand facility context and review of electronic records to determine costs. We calculated initial costs to establish EHS and annual operations and maintenance.Available records contained little information on the upfront, capital costs associated with establishing EHS. Annual operations and maintenance totaled USD 220,427 for all EHS across all facilities (USD 5.21 per patient encounter), although costs of many essential inputs were missing from records. Annual operations and maintenance costs were highest for cleaning  and waste management .Missing expenses suggests that documented costs are substantial underestimates. Costs to establish services were missing predominantly because purchases pre-dated electronic records. Annual operations and maintenance costs were incomplete primarily because administrative records did not record sufficient detail to disaggregate and attribute expenses.Electronic health information systems have potential to support efficient data collection. However, we found that existing records systems were decentralized and poorly suited to identify EHS costs. Our research suggests a need to better code and disaggregate EHS expenses to properly leverage records for costing. Frameworks developed in this study are a potential tool to develop more accurate estimates of the cost of providing EHS in HCFs.The online version contains supplementary material available at 10.1186/s12913-021-06325-3. Contributions to the literatureWe developed frameworks of expenses required to deliver environmental health services in healthcare facilities in low- and middle-income countries that can be used in future studies to assess completeness and accuracy of costs dataWater, sanitation, hand hygiene and waste management require more upfront expenses for infrastructure than cleaning, personal protective equipment use, and vector control, which require predominantly personnel and consumable supply expensesElectronic health information systems have potential to support efficient data collection, but incompleteness and poor disaggregation of environmental expenses impede costingA hygienic environment is critical for safe healthcare provision. Two-thirds of healthcare acquired infections (HAIs) are attributable to inadequate hand hygiene of healthcare workers and environmental surface contamination . An estiEnvironmental health services (EHS) in healthcare facilities (HCFs) reduce the risk of HAIs by reducing transmission of contamination from person-to-person, and person-to-environment and vice versa , 5. EHS Goal 6 of the Sustainable Development Goals (SDGs) sets targets for achieving universal access to water, sanitation, and hygiene. The World Health Organization (WHO) and UNICEF’s Joint Monitoring Program for Water Supply, Sanitation, and Hygiene (JMP), which monitors progress towards SDG targets 6.1 and 6.2, interprets “universal” to include HCF settings and also monitors waste management and cleaning as EHS. HCFs have been specifically identified as a priority in the United Nations’ Decade for Action to meet the SDGs by 2030 , 15. TheAdequate financing is critical to improving access , 18. HowWe describe the first application of a model designed specifically for costing EHS in HCFs in LMICs . We exteWe collected data in a network of facilities operated by University of North Carolina (UNC) Project Malawi. This is a collaboration between UNC-Chapel Hill and the government-run central hospital in Lilongwe. UNC Project’s primary purpose is to support medical research and training, and it provides free clinical care to patients enrolled in medical research studies, with study populations including patients with HIV/AIDS, malaria, tuberculosis, and cancer. Because patients are research participants, they have longer visits and receive additional laboratory work than the general patient population. In 2018, UNC Project recorded 42,228 patient encounters, 25,849 patient care hours, and 7268 pathology reports.UNC Project facilities comprise seven buildings, with 70 rooms for outpatient care and a research facility with offices and a 370 square-meter laboratory. The majority of operating space is laboratory, pharmacy, teaching, and administrative. Additional setting description is provided in Additional file We reviewed selected guidelines for environmental health and infection prevention and control , 20–37 tPlanning to define the scope of relevant EHS and costs, to understand the facility context, and to develop a data collection plan; Data collection to execute the data collection plan and evaluate data quality; and Synthesis to calculate costs and disseminate findings. Anderson et al.’s model is designed to be flexible to a variety of contexts and methods. This study employed a mixed-methods approach comprising qualitative interviews to understand facility context and a retrospective records review for costs data collection.We followed a ten-step process model described by Anderson et al. . BrieflyWe describe the specific methods used for each step below and in Additional files We assessed initial costs to establish services and subsequent costs for operations and maintenance. We identified UNC Project as our setting because they provided all EHS outputs that we identified as essential and had electronic records available. We present costs at the network level for EHS across all facilities within UNC Project, as records of disbursement of resources across individual facilities within the network were not available. Where costs were unavailable through records, as was the case for most capital costs, we describe the reasons for missingness and propose good practices for collecting this information in future studies.Essential Environmental Standards in Healthcare Facilities [We considered the following as EHS, based on the WHO’s cilities : water, We examined costs from the perspective of the HCF network and captured only the expenses documented in electronic records systems. We did not consider economic costs or financial costs incurred outside the HCF network. We defined the timeframe of eligible costs as installation and subsequent annual operations and maintenance. We defined eligible types of expenses as any expense falling into one of the nine categories described in Table We collected contextual data on the resources required to deliver each EHS and the quantity and quality of EHS in network facilities through interviews with facility staff, including the facility director, administrative officers, head nurse, and staff from the accounting and procurement departments. Interview guides were designed to capture information on modes of EHS provision, resources used, and any challenges with service adequacy or safety. A list of the job titles of participants and the interview guide are provided in Additional file We assessed EHS quality by reviewing interview recordings and noting instances where interviewees commented on the safety, reliability, or sustainability of services. We collected information on the number and purpose of clinical and non-clinical rooms in each facility and the number and type of staff employed. Information on patient volume was gathered from internal annual reports. Descriptions of EHS resource inputs, quality, and quantity are provided in Additional file We pilot tested our costing tools and protocol in three out-of-network HCFs  prior to data collection at UNC Project. We pilot tested a survey instrument designed to conduct bottom-up costing but found that staff recall was insufficient. Through subsequent discussions with facility administrators, we identified retrospective records review as the best data collection strategy based on accessibility of electronic records and level of detail recorded.We identified departments responsible for purchasing resources identified in our costing frameworks, which comprised the laboratory, pharmacy, general stores, and administrative departments. We interviewed department heads, administrators, and accounting staff to determine how departments maintained financial and/or inventory records. Each department maintained an independent records system. Records ranged from January 2016 to June 2018 and covered an average of 17 months per department. Administrators provided spreadsheets with costs data for personnel salaries and budgets for recurrent trainings relevant to EHS for the most recent year (2018).We exported records from each department into Microsoft Excel . We asked staff to qualitatively evaluate records’ accuracy and completeness, and we restricted our exports to time periods where staff evaluated records to be accurate.R statistical software version 3.5.0 . We reviewed and coded all unique line items (n = 9290) to identify any expenses relevant to an EHS. Within each EHS, we subsequently coded these relevant expenses into the cost categories described in Table All departmental records contained lists of line items for purchases made by UNC Project. Line items were not coded or organized in a way that allowed us to automatically disaggregate EHS expenses, and we therefore coded all records by hand. We extracted unique item descriptions from each set of records using To assess the accuracy and representativity of cost data, we used the iterative process described by Anderson et al. . In brieWe calculated costs at the network level, aggregated across all facilities. Records tracked central purchasing of goods and services at the network level. Disbursement of resources to individual facilities was not tracked in these records, and we therefore did not estimate costs at the facility level.We divided costs into capital hardware and software costs to establish EHS and annual operations and maintenance costs. We found no financing costs relevant to EHS and therefore excluded this category from our cost calculations. We also found little information on capital hardware and software costs in records. Thus, we do not present a full summary of these costs in the results below, though the full datasets for each EHS, including capital costs where available, are provided in Additional files Annual operations and maintenance costs comprised the costs categories of capital maintenance, recurrent training, consumables, personnel, direct support, and contracted services. For capital maintenance, consumables, personnel, and contracted services, we calculated the average monthly cost based on the data period and multiplied by 12 to obtain annual costs. We used annual budgets for safety trainings for recurrent training costs. Safety trainings were relevant to four EHS: hygiene, PPE, cleaning, and waste management. While curricula for safety trainings covered all EHS simultaneously, we included the full training costs under each EHS, as each EHS required annual training, and costs would not be substantially reduced by removing irrelevant curricula. Waste management had additional costs associated with consumables and capital maintenance for waste transport vehicles, which we estimated using established methods .Personnel costs were adjusted based on the proportion of effort dedicated to each EHS . Personnel costs included salaries only, not employment benefits. For direct support, we estimated the proportion of effort for supervisors, procurement officers, and logistics officers involved in EHS provision at 1% for EHS requiring substantial supervision of personnel (cleaning and waste management) and 0.5% for all others.All costs recorded in Malawi kwacha (MWK) were converted into United States dollars ($) using the exchange rate for January 1 in the year during which costs were incurred . All costs were adjusted for inflation to January 2019.Additional calculation details are in Additional file We conducted an internal review of data with facility stakeholders. We presented records to administrative and accounting staff and asked them to verify that data were accurate and complete to the best of their knowledge and that missing data were not readily available in records systems.Table Through identifying essential outputs and inputs for EHS delivery in framework development, we found that EHS divided into two categories—“capital-heavy” versus “personnel-heavy”—based on the predominant type of resources required for service provision. Capital-heavy EHS required substantial resources for upfront capital hardware, capital software, and ongoing capital maintenance but fewer resources for personnel and consumables. They comprised water, sanitation, hand hygiene, waste management, and laundry, which required infrastructure installation with substantial costs . Personnel-heavy EHS were those with minimal resources required upfront to establish services, but higher demand for personnel in routine operations. They comprised PPE, cleaning, and vector control.Resource needs for consumables were high for personnel-heavy EHS , though waste management and hand hygiene also required substantial consumable resource inputs. Resource needs for recurrent training were comparable across EHS. Direct support outputs were most commonly safety planning, monitoring, and inspections, for which the primary expenses are staff time, though some direct support required purchase of physical goods and services .We provide summary measures of recorded expenses and identify missing data in Table Records contained little information on the capital hardware and software costs associated with the estabilishment of EHS. Often, capital hardware costs were for infrastructure purchased and installed as a part of facility construction , and construction-related costs were not included in electronic records that we obtained for this study. Relevant capital software costs  were similarly incurred at the time of construction and not in electronic records.For the capital hardware and software costs of equipment purchased after facility construction, records captured few relevant costs. Several low-cost items for PPE, cleaning, and waste management were purchased frequently throughout the timespan of available records  but were not representative of overall capital hardware costs.For most capital hardware items with higher expected expenses  available records typically did not contain relevant expenses, likely because items were purchased before records systems were established and their lifespan exceeded the duration of records. We found some recorded examples of capital hardware costs, such as $116 for the purchase of a waste trolley. However, as many expected capital hardware costs were not captured, we do not provide summary measures here. Evidence for the few examples we did find documented are included in Additional files Summaries of the total annual operations and maintenance costs for each EHS are presented in Table Annual operations and maintenance costs were highest for cleaning and waste management, though they had the fewest missing expenses. For both, personnel costs were a substantial proportion of overall expenses. Subsequent sections provide a breakdown of record-keeping practices and available data by cost category.Most capital maintenance costs were missing from records. We relied on line-item descriptions to identify and extract capital maintenance costs, and these descriptions were often insufficient to identify the type of maintenance performed or determine its relevance to EHS. For example, we found line items for unspecified building maintenance and repairs, but rarely was the work type or repair target described. Where the specific nature of the maintenance was described and relevant to EHS , we included these line items as capital maintenance expenses. Due to the high frequency of unspecified maintenance and repair expenses, we judge these capital maintenance costs to be substantial underestimates.Recurrent training comprised an annual “safety” training, covering topics related to safe handling of hazardous materials , laboratory and clinical specimens, and proper use of PPE. Training costs included facilitator fees, allowances for attendees, and food. Training curricula were relevant to hygiene, PPE, waste management, and cleaning, and we included the full training costs under each of these EHS. We found no record of recurrent trainings for other EHS, although we expected recurrent training for all EHS. Total annual costs of safety training were $1821 for three training sessions, one each for clinical and pharmacy staff, laboratory staff, and clinical support staff and waste handlers.Consumables contributed the most to overall annual operations and maintenance expenses for all EHS except waste management and cleaning; and consumables were the expenses most consistently recorded in electronic records. Consumables costs were highest for water  and PPE . Water consumables were predominantly water utility bills , while PPE consumables were more diverse. Consumable costs for vector control were several orders of magnitude lower than other EHS ($473), and vector control costs were primarily for contracted services.We identified personnel costs for cleaning and waste management totaling $46,968 and $22,273 annually, respectively. Clinic aides were primarily responsible for cleaning and waste management activities, but nurses, laboratory and pharmacy technicians performing supervisory and specialized tasks  contributed a higher percentage of costs due to the high number of nurses employed and their higher salaries. Interviews suggested that the proportional effort that staff dedicated to other EHS was negligible and was therefore not included , or personnel costs were included in contracted services and could not be disaggregated (laundry and vector control).Direct support costs were estimated as $1057 each for water, sanitation, hygiene, PPE, and vector control, and $2114 for cleaning and waste management. These costs represent the proportional effort of supervisory, procurement, and logistics staff. Costs of goods and external services required for direct support  were not available in records and are not included in cost estimates.We applied a model designed specifically for costing EHS  in a netFor all EHS, we found that a substantial number of essential expenses were missing from records—particularly capital hardware, capital software, and capital maintenance costs—indicating that the cost estimates reported here are substantial underestimates of the full financial costs of EHS delivery within the HCF network.We attribute missing information in the electronic records to several factors. Capital hardware and software costs incurred at the time of facility construction were not documented in the electronic records obtained for this study. Contractors maintained detailed records of construction-related costs at the time of construction, which contained EHS-related expenses, but only summaries were retained by UNC Project. Any item or service purchased by UNC Project from UNC-Chapel Hill directly is not recorded in systems from which we obtained records. In particular, large equipment for capital hardware is typically purchased directly through UNC-Chapel Hill purchasing services, and these transactions are not recorded by UNC Project. Obtaining records from outside the UNC Project system was beyond the scope of this study. However, future studies could document these costs by prospectively documenting relevant costs for new construction or major rehabilitations, contacting contractors to obtain additional records or estimates, or examining audited financial statements as potential data sources. In calculating capital costs, future studies will also need to take additional steps to properly account for depreciation of hardware.Records only covered an average of 17 months of data, which is likely insufficient to capture infrequent expenses. Capturing lifecycle costs of EHS remains a challenge, as the lifespan of EHS infrastructure can be several decades, making prospective data collection difficult. Electronic records systems for retrospective data collection are uncommon in Malawi and other LMICs , and lacWe attribute missing information on the costs associated with operations and maintenance in part to inconsistent labeling and disaggregation of EHS expenses and completeness of records. For example, capital maintenance costs were often missing because records did not contain sufficient detail to determine the type of maintenance performed and attribute it to a specific EHS. Another plausible explanation for missing expenses is inadequate service delivery . Evidence from public facilities in Malawi indicates that shortages of PPE, soap for handwashing, and other EHS are common –44. SimiWe identified personnel costs only for cleaning and waste management. Laundry and vector control would have likely incurred substantial personnel costs if provided in-house, but they were paid as a single fee for contracted services that covered all costs categories. In our qualitative assessment from Step 4, we determined that the personnel costs associated with other EHS were negligible. However, frequent tasks individually requiring little time  contribute to costs and in large facilities may sum to substantial amounts. Time and motion studies, activity diaries, or other observational techniques could be used to identify these costs .We classified some expenses as capital hardware but observed frequent repurchasing, notably for cleaning tools , reusable PPE, and waste bins. These items fit our definition of capital hardware in that they “are not consumed during normal service operation.” However, frequent repurchasing suggests they have a short lifecycle and/or are disposed rather than repaired. Given frequent repurchasing, these items may alternatively be considered consumable costs. When considered consumable products, these items respectively contribute $1197, $1121, and $1698 to PPE, waste management, and cleaning operations and maintenance costs annually . FoFrom our framework development, we found that EHS fell into two type of expense profiles: capital-heavy versus personnel-heavy. We found cleaning, vector control, and PPE were personnel-heavy. However, this distinction will be sensitive to several factors, including the modality of service provision and relative prices of labor versus goods. For example, we found hand hygiene to be a capital-heavy EHS due to infrastructure inputs for sinks with piped water. Hand hygiene provided through lower-cost infrastructure that is not connected to a piped network may shift the majority of costs to other categories.Whether EHS are capital- or personnel-heavy has implications for investment in EHS improvements. Personnel-heavy EHS will have lower upfront financial barriers to establish or conduct major infrastructure rehabilitations or improvements, and may therefore be more feasible in settings where funds for substantial capital investment are lacking. Research on lifecycle costs, as well as cost-effectiveness of various EHS, is lacking but would inform investment decisions This study captures only the costs that were recorded in electronic records systems. A majority of capital hardware and capital software expenses were missing from these records, as well as a substantial number of expenses from other costs categories. A bottom-up costing approach, which identifies the unit costs and quantities of each resource input, would allow for more granular detail regarding specific expenditures , but we As UNC Project provides healthcare in the context of clinical research trials, the profile of expenses in non-research focused HCFs may differ. UNC Project employs a high number of administrative and research personnel that do not interact with patients. These personnel and their associated building spaces contribute to EHS costs differently than clinical staff and spaces . Public HCFs in Malawi would be more generalizable to HCFs in other LMICs. However, during pilot testing we found that recall and paper-based records in public facilities were insufficient to support costing. LMICs where public facilities have stronger electronic HMIS are likely more feasible settings for future costing research.We were unable to account for how resources were used within UNC Project after purchase at the network level. UNC Project facilities are separate buildings or designated rooms solely for UNC Project use within government facilities. However, some UNC Project staff did work in the wards of government-run facilities and collaborate with public HCF employees. While UNC Project was not responsible for ensuring adequate EHS outside of designated rooms in government facilities, our interviews indicated that resources from UNC Project were sometimes diverted to compensate for inadequate conditions in government facilities. For example, although the government was responsible for ensuring functional toilets in buildings where UNC Project had designated rooms, UNC Project reported paying for repairs when the government was delayed or unable to ensure functionality. We were unable to determine the value of goods diverted out-of-network but recognize that this will result in overestimates of the true costs of achieving adequate EHS in UNC Project facilities.We categorized expenses under the EHS where we judged them to be most used, based on our frameworks. For example, we classified all recorded PPE expenses as PPE at the point of care, although we know some proportion of PPE were used for cleaning, waste management, and potentially other applications. We assumed that products were used as marketed. For example, we assumed that surface cleaning soaps were used for surface cleaning and hand soaps were used for handwashing, although in some cases these products may be used interchangeably. This may result in minor misclassification of items and error in the costs per EHS but should not change the combined total for all EHS.We applied a process model designed specifically for costing EHS in HCFs , and we We attempted to estimate the costs to establish, operate, and maintain eight EHS, using available electronic records. However, due to record-keeping practices and short timeframes of available records, our estimates for certain cost categories are likely substantial underestimates. Data collection through other means, for example bottom-up costing using surveys and observations, may yield more complete estimates. However, bottom-up costing can compensate for limitations of records but can be challenging due to high staff turnover and poor recall, and is time- and resource-intensive. Electronic HMIS have the potential to facilitate more efficient data collection, but our research suggests a need to better code and disaggregate EHS expenses to fully leverage these systems.We used our costing frameworks primarily to assess completeness of records. We assessed only presence/absence of expenses, but our frameworks could be adapted to assess magnitude. Such an adaptation would require additional research on the correlation between variables such as facility size, patient volume, and type of healthcare services offered, which influence the quantity and quality of EHS needed but in ways that are poorly understood. Given low priority often assigned to EHS in low-resource settings , 51, docAdditional file 1. EHS & facility building descriptionsAdditional file 2. Interview guide and participant descriptionsAdditional file 3. Additional costing methodsAdditional file 4. EHS costing frameworksAdditional file 5. CHEERS checklistAdditional file 6. Disaggregated costs for waterAdditional file 7. Disaggregated costs for sanitationAdditional file 8. Disaggregated costs for hygieneAdditional file 9. Disaggregated costs for PPEAdditional file 10. Disaggregated costs for waste managementAdditional file 11. Disaggregated costs for cleaningAdditional file 12. Disaggregated costs for laundryAdditional file 13. Disaggregated costs for vector control