Diversity and ecological footprint of Global Ocean RNA viruses.

DNA viruses are increasingly recognized as influencing marine microbes and microbe-mediated biogeochemical cycling. However, little is known about global marine RNA virus diversity, ecology, and ecosystem roles. In this study, we uncover patterns and predictors of marine RNA virus community- and "species"-level diversity and contextualize their ecological impacts from pole to pole. Our analyses revealed four ecological zones, latitudinal and depth diversity patterns, and environmental correlates for RNA viruses. Our findings only partially parallel those of cosampled plankton and show unexpectedly high polar ecological interactions. The influence of RNA viruses on ecosystems appears to be large, as predicted hosts are ecologically important. Moreover, the occurrence of auxiliary metabolic genes indicates that RNA viruses cause reprogramming of diverse host metabolisms, including photosynthesis and carbon cycling, and that RNA virus abundances predict ocean carbon export.

Cryptic and abundant marine viruses at the evolutionary origins of Earth's RNA virome.

Whereas DNA viruses are known to be abundant, diverse, and commonly key ecosystem players, RNA viruses are insufficiently studied outside disease settings. In this study, we analyzed ≈28 terabases of Global Ocean RNA sequences to expand Earth's RNA virus catalogs and their taxonomy, investigate their evolutionary origins, and assess their marine biogeography from pole to pole. Using new approaches to optimize discovery and classification, we identified RNA viruses that necessitate substantive revisions of taxonomy (doubling phyla and adding >50% new classes) and evolutionary understanding. "Species"-rank abundance determination revealed that viruses of the new phyla "Taraviricota," a missing link in early RNA virus evolution, and "Arctiviricota" are widespread and dominant in the oceans. These efforts provide foundational knowledge critical to integrating RNA viruses into ecological and epidemiological models.

Expanding standards in viromics: in silico evaluation of dsDNA viral genome identification, classification, and auxiliary metabolic gene curation.

BACKGROUND: Viruses influence global patterns of microbial diversity and nutrient cycles. Though viral metagenomics (viromics), specifically targeting dsDNA viruses, has been critical for revealing viral roles across diverse ecosystems, its analyses differ in many ways from those used for microbes. To date, viromics benchmarking has covered read pre-processing, assembly, relative abundance, read mapping thresholds and diversity estimation, but other steps would benefit from benchmarking and standardization. Here we use in silico-generated datasets and an extensive literature survey to evaluate and highlight how dataset composition (i.e., viromes vs bulk metagenomes) and assembly fragmentation impact (i) viral contig identification tool, (ii) virus taxonomic classification, and (iii) identification and curation of auxiliary metabolic genes (AMGs). RESULTS: The in silico benchmarking of five commonly used virus identification tools show that gene-content-based tools consistently performed well for long (≥3 kbp) contigs, while k-mer- and blast-based tools were uniquely able to detect viruses from short (≤3 kbp) contigs. Notably, however, the performance increase of k-mer- and blast-based tools for short contigs was obtained at the cost of increased false positives (sometimes up to ∼5% for virome and ∼75% bulk samples), particularly when eukaryotic or mobile genetic element sequences were included in the test datasets. For viral classification, variously sized genome fragments were assessed using gene-sharing network analytics to quantify drop-offs in taxonomic assignments, which revealed correct assignations ranging from ∼95% (whole genomes) down to ∼80% (3 kbp sized genome fragments). A similar trend was also observed for other viral classification tools such as VPF-class, ViPTree and VIRIDIC, suggesting that caution is warranted when classifying short genome fragments and not full genomes. Finally, we highlight how fragmented assemblies can lead to erroneous identification of AMGs and outline a best-practices workflow to curate candidate AMGs in viral genomes assembled from metagenomes. CONCLUSION: Together, these benchmarking experiments and annotation guidelines should aid researchers seeking to best detect, classify, and characterize the myriad viruses 'hidden' in diverse sequence datasets.

The Influence of Weather on the Occurrence of Aflatoxin B1 in Harvested Maize from Kenya and Tanzania.

A study was conducted using maize samples collected from different agroecological zones of Kenya (n = 471) and Tanzania (n = 100) during the 2013 maize harvest season to estimate a relationship between aflatoxin B1 concentration and occurrence with weather conditions during the growing season. The toxins were analysed by the ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method. Aflatoxin B1 incidence ranged between 0-100% of samples in different regions with an average value of 29.4% and aflatoxin concentrations of up to 6075 µg/kg recorded in one sample. Several regression techniques were explored. Random forests achieved the highest overall accuracy of 80%, while the accuracy of a logistic regression model was 65%. Low rainfall occurring during the early stage of the maize plant maturing combined with high temperatures leading up to full maturity provide warning signs of aflatoxin contamination. Risk maps for the two countries for the 2013 season were generated using both random forests and logistic regression models.

Viral Ecogenomics of Arctic Cryopeg Brine and Sea Ice.

Arctic regions, which are changing rapidly as they warm 2 to 3 times faster than the global average, still retain microbial habitats that serve as natural laboratories for understanding mechanisms of microbial adaptation to extreme conditions. Seawater-derived brines within both sea ice (sea-ice brine) and ancient layers of permafrost (cryopeg brine) support diverse microbes adapted to subzero temperatures and high salinities, yet little is known about viruses in these extreme environments, which, if analogous to other systems, could play important evolutionary and ecosystem roles. Here, we characterized viral communities and their functions in samples of cryopeg brine, sea-ice brine, and melted sea ice. Viral abundance was high in cryopeg brine (1.2 × 108 ml-1) and much lower in sea-ice brine (1.3 × 105 to 2.1 × 105 ml-1), which roughly paralleled the differences in cell concentrations in these samples. Five low-input, quantitative viral metagenomes were sequenced to yield 476 viral populations (i.e., species level; ≥10 kb), only 12% of which could be assigned taxonomy by traditional database approaches, indicating a high degree of novelty. Additional analyses revealed that these viruses: (i) formed communities that differed between sample type and vertically with sea-ice depth; (ii) infected hosts that dominated these extreme ecosystems, including Marinobacter, Glaciecola, and Colwellia; and (iii) encoded fatty acid desaturase (FAD) genes that likely helped their hosts overcome cold and salt stress during infection, as well as mediated horizontal gene transfer of FAD genes between microbes. Together, these findings contribute to understanding viral abundances and communities and how viruses impact their microbial hosts in subzero brines and sea ice.IMPORTANCE This study explores viral community structure and function in remote and extreme Arctic environments, including subzero brines within marine layers of permafrost and sea ice, using a modern viral ecogenomics toolkit for the first time. In addition to providing foundational data sets for these climate-threatened habitats, we found evidence that the viruses had habitat specificity, infected dominant microbial hosts, encoded host-derived metabolic genes, and mediated horizontal gene transfer among hosts. These results advance our understanding of the virosphere and how viruses influence extreme ecosystems. More broadly, the evidence that virally mediated gene transfers may be limited by host range in these extreme habitats contributes to a mechanistic understanding of genetic exchange among microbes under stressful conditions in other systems.

A metagenomic study of DNA viruses from samples of local varieties of common bean in Kenya.

Common bean (Phaseolus vulgaris L.) is the primary source of protein and nutrients in the majority of households in sub-Saharan Africa. However, pests and viral diseases are key drivers in the reduction of bean production. To date, the majority of viruses reported in beans have been RNA viruses. In this study, we carried out a viral metagenomic analysis on virus symptomatic bean plants. Our virus detection pipeline identified three viral fragments of the double-stranded DNA virus Pelargonium vein banding virus (PVBV) (family, Caulimoviridae, genus Badnavirus). This is the first report of the dsDNA virus and specifically PVBV in legumes to our knowledge. In addition two previously reported +ssRNA viruses the bean common mosaic necrosis virus (BCMNVA) (Potyviridae) and aphid lethal paralysis virus (ALPV) (Dicistroviridae) were identified. Bayesian phylogenetic analysis of the Badnavirus (PVBV) using amino acid sequences of the RT/RNA-dependent DNA polymerase region showed the Kenyan sequence (SRF019_MK014483) was closely matched with two Badnavirus viruses: Dracaena mottle virus (DrMV) (YP_610965) and Lucky bamboo bacilliform virus (ABR01170). Phylogenetic analysis of BCMNVA was based on amino acid sequences of the Nib region. The BCMNVA phylogenetic tree resolved two clades identified as clade (I and II). Sequence from this study SRF35_MK014482, clustered within clade I with other Kenyan sequences. Conversely, Bayesian phylogenetic analysis of ALPV was based on nucleotide sequences of the hypothetical protein gene 1 and 2. Three main clades were resolved and identified as clades I-III. The Kenyan sequence from this study (SRF35_MK014481) clustered within clade II, and nested within a sub-clade; comprising of sequences from China and an earlier ALPV sequences from Kenya isolated from maize (MF458892). Our findings support the use of viral metagenomics to reveal the nascent viruses, their viral diversity and evolutionary history of these viruses. The detection of ALPV and PVBV indicate that these viruses have likely been underreported due to the unavailability of diagnostic tools.

Evolutionary insights of Bean common mosaic necrosis virus and Cowpea aphid-borne mosaic virus.

Plant viral diseases are one of the major limitations in legume production within sub-Saharan Africa (SSA), as they account for up to 100% in production losses within smallholder farms. In this study, field surveys were conducted in the western highlands of Kenya with viral symptomatic leaf samples collected. Subsequently, next-generation sequencing was carried out to gain insights into the molecular evolution and evolutionary relationships of Bean common mosaic necrosis virus (BCMNV) and Cowpea aphid-borne mosaic virus (CABMV) present within symptomatic common bean and cowpea. Eleven near-complete genomes of BCMNV and two for CABMV were obtained from western Kenya. Bayesian phylogenomic analysis and tests for differential selection pressure within sites and across tree branches of the viral genomes were carried out. Three well-supported clades in BCMNV and one supported clade for CABMNV were resolved and in agreement with individual gene trees. Selection pressure analysis within sites and across phylogenetic branches suggested both viruses were evolving independently, but under strong purifying selection, with a slow evolutionary rate. These findings provide valuable insights on the evolution of BCMNV and CABMV genomes and their relationship to other viral genomes globally. The results will contribute greatly to the knowledge gap involving the phylogenomic relationship of these viruses, particularly for CABMV, for which there are few genome sequences available, and inform the current breeding efforts towards resistance for BCMNV and CABMV.

Phylogenomic relationship and evolutionary insights of sweet potato viruses from the western highlands of Kenya.

Sweet potato is a major food security crop within sub-Saharan Africa where 90% of Africa production occurs. One of the major limitations of sweet potato production are viral infections. In this study, we used a combination of whole genome sequences from a field isolate obtained from Kenya and those available in GenBank. Sequences of four sweet potato viruses: Sweet potato feathery mottle virus (SPFMV), Sweet potato virus C (SPVC), Sweet potato chlorotic stunt virus (SPCSV), Sweet potato chlorotic fleck virus (SPCFV) were obtained from the Kenyan sample. SPFMV sequences both from this study and from GenBank were found to be recombinant. Recombination breakpoints were found within the Nla-Pro, coat protein and P1 genes. The SPCSV, SPVC, and SPCFV viruses from this study were non-recombinant. Bayesian phylogenomic relationships across whole genome trees showed variation in the number of well-supported clades; within SPCSV (RNA1 and RNA2) and SPFMV two well-supported clades (I and II) were resolved. The SPCFV tree resolved three well-supported clades (I-III) while four well-supported clades were resolved in SPVC (I-IV). Similar clades were resolved within the coalescent species trees. However, there were disagreements between the clades resolved in the gene trees compared to those from the whole genome tree and coalescent species trees. However the coat protein gene tree of SPCSV and SPCFV resolved similar clades to the genome and coalescent species tree while this was not the case in SPFMV and SPVC. In addition, we report variation in selective pressure within sites of individual genes across all four viruses; overall all viruses were under purifying selection. We report the first complete genomes of SPFMV, SPVC, SPCFV, and a partial SPCSV from Kenya as a mixed infection in one sample. Our findings provide a snap shot on the evolutionary relationship of sweet potato viruses (SPFMV, SPVC, SPCFV, and SPCSV) from Kenya as well as assessing whether selection pressure has an effect on their evolution.

Zoonotic surveillance for rickettsiae in domestic animals in Kenya.

Abstract Rickettsiae are obligate intracellular bacteria that cause zoonotic and human diseases. Arthropod vectors, such as fleas, mites, ticks, and lice, transmit rickettsiae to vertebrates during blood meals. In humans, the disease can be life threatening. This study was conducted amidst rising reports of rickettsioses among travelers to Kenya. Ticks and whole blood were collected from domestic animals presented for slaughter at major slaughterhouses in Nairobi and Mombasa that receive animals from nearly all counties in the country. Blood samples and ticks were collected from 1019 cattle, 379 goats, and 299 sheep and were screened for rickettsiae by a quantitative PCR (qPCR) assay (Rick17b) using primers and probe that target the genus-specific 17-kD gene (htrA). The ticks were identified using standard taxonomic keys. All Rick17b-positive tick DNA samples were amplified and sequenced with primers sets that target rickettsial outer membrane protein genes (ompA and ompB) and the citrate-synthase encoding gene (gltA). Using the Rick17b qPCR, rickettsial infections in domestic animals were found in 25/32 counties sampled (78.1% prevalence). Infection rates were comparable in cattle (16.3%) and sheep (15.1%) but were lower in goats (7.1%). Of the 596 ticks collected, 139 had rickettsiae (23.3%), and the detection rates were highest in Amblyomma (62.3%; n=104), then Rhipicephalus (45.5%; n=120), Hyalomma (35.9%; n=28), and Boophilus (34.9%; n=30). Following sequencing, 104 out of the 139 Rick17b-positive tick DNA had good reverse and forward sequences for the 3 target genes. On querying GenBank with the generated consensus sequences, homologies of 92-100% for the following spotted fever group (SFG) rickettsiae were identified: Rickettsia africae (93.%, n=97), Rickettsia aeschlimannii (1.9%, n=2), Rickettsia mongolotimonae (0.96%, n=1), Rickettsia conorii subsp. israelensis (0.96%, n=1), Candidatus Rickettsia kulagini (0.96% n=1), and Rickettsia spp. (1.9% n=2). In conclusion, molecular methods were used in this study to detect and identify rickettsial infections in domestic animals and ticks throughout Kenya.