Maize lethal necrosis is caused by a synergistic co-infection of Maize chlorotic mottle virus (MCMV) and a specific member of the Potyviridae, such as Sugarcane mosaic virus (SCMV), Wheat streak mosaic virus (WSMV) or Johnson grass mosaic virus (JGMV). Typical maize lethal necrosis symptoms include severe yellowing and leaf drying from the edges. In Kenya, we detected plants showing typical and atypical symptoms. Both groups of plants often tested negative for SCMV by ELISA. We used next-generation sequencing to identify viruses associated to maize lethal necrosis in Kenya through a metagenomics analysis. Symptomatic and asymptomatic leaf samples were collected from maize and sorghum representing sixteen counties. Complete and partial genomes were assembled for MCMV, SCMV, Maize streak virus (MSV) and Maize yellow dwarf virus-RMV (MYDV-RMV). These four viruses (MCMV, SCMV, MSV and MYDV-RMV) were found together in 30 of 68 samples. A geographic analysis showed that these viruses are widely distributed in Kenya. Phylogenetic analyses of nucleotide sequences showed that MCMV, MYDV-RMV and MSV are similar to isolates from East Africa and other parts of the world. Single nucleotide polymorphism, nucleotide and polyprotein sequence alignments identified three genetically distinct groups of SCMV in Kenya. Variation mapped to sequences at the border of NIb and the coat protein. Partial genome sequences were obtained for other four potyviruses and one polerovirus. Our results uncover the complexity of the maize lethal necrosis epidemic in Kenya. MCMV, SCMV, MSV and MYDV-RMV are widely distributed and infect both maize and sorghum. SCMV population in Kenya is diverse and consists of numerous strains that are genetically different to isolates from other parts of the world. Several potyviruses, and possibly poleroviruses, are also involved.

The African eggplant (Solanum aethiopicum) is a nutritious traditional vegetable used in many African countries, including Uganda and Nigeria. It is thought to have been domesticated in Africa from its wild relative, Solanum anguivi. S. aethiopicum has been routinely used as a source of disease resistance genes for several Solanaceae crops, including Solanum melongena. A lack of genomic resources has meant that breeding of S. aethiopicum has lagged behind other vegetable crops. We assembled a 1.02-Gb draft genome of S. aethiopicum, which contained predominantly repetitive sequences (78.9%). We annotated 37,681 gene models, including 34,906 protein-coding genes. Expansion of disease resistance genes was observed via 2 rounds of amplification of long terminal repeat retrotransposons, which may have occurred ∼1.25 and 3.5 million years ago, respectively. By resequencing 65 S. aethiopicum and S. anguivi genotypes, 18,614,838 single-nucleotide polymorphisms were identified, of which 34,171 were located within disease resistance genes. Analysis of domestication and demographic history revealed active selection for genes involved in drought tolerance in both “Gilo” and “Shum” groups. A pan-genome of S. aethiopicum was assembled, containing 51,351 protein-coding genes; 7,069 of these genes were missing from the reference genome. The genome sequence of S. aethiopicum enhances our understanding of its biotic and abiotic resistance. The single-nucleotide polymorphisms identified are immediately available for use by breeders. The information provided here will accelerate selection and breeding of the African eggplant, as well as other crops within the Solanaceae family.

One of the major challenges facing investigators in the microbiome field is turning large numbers of reads generated by next-generation sequencing (NGS) platforms into biological knowledge. Effective analytical workflows that guarantee reproducibility, repeatability, and result provenance are essential requirements of modern microbiome research. For nearly a decade, several state-of-the-art bioinformatics tools have been developed for understanding microbial communities living in a given sample. However, most of these tools are built with many functions that require an in-depth understanding of their implementation and the choice of additional tools for visualizing the final output. Furthermore, microbiome analysis can be time-consuming and may even require more advanced programming skills which some investigators may be lacking. We have developed a wrapper named iMAP (Integrated Microbiome Analysis Pipeline) to provide the microbiome research community with a user-friendly and portable tool that integrates bioinformatics analysis and data visualization. The iMAP tool wraps functionalities for metadata profiling, quality control of reads, sequence processing and classification, and diversity analysis of operational taxonomic units. This pipeline is also capable of generating web-based progress reports for enhancing an approach referred to as review-as-you-go (RAYG). For the most part, the profiling of microbial community is done using functionalities implemented in Mothur or QIIME2 platform. Also, it uses different R packages for graphics and R-markdown for generating progress reports. We have used a case study to demonstrate the application of the iMAP pipeline. The iMAP pipeline integrates several functionalities for better identification of microbial communities present in a given sample. The pipeline performs in-depth quality control that guarantees high-quality results and accurate conclusions. The vibrant visuals produced by the pipeline facilitate a better understanding of the complex and multidimensional microbiome data. The integrated RAYG approach enables the generation of web-based reports, which provides the investigators with the intermediate output that can be reviewed progressively. The intensively analyzed case study set a model for microbiome data analysis.

Carica papaya L. is an important fruit crop grown by small- and large-scale farmers in Kenya for local and export markets. However, its production is constrained by papaya ringspot disease (PRSD). The disease is believed to be caused by papaya ringspot virus (PRSV). Previous attempts to detect PRSV in papaya plants showing PRSD symptoms, using enzyme-linked immunosorbent assay (ELISA) and reverse transcriptase-polymerase chain reaction (RT-PCR) procedures with primers specific to PRSV, have not yielded conclusive results. Therefore, the nature of viruses responsible for PRSD was elucidated in papaya leaves collected from 22 counties through Illumina MiSeq next-generation sequencing (NGS) and validated by RT-PCR and Sanger sequencing. Viruses were detected in 38 out of the 48 leaf samples sequenced. Sequence analysis revealed the presence of four viruses: a Potyvirus named Moroccan watermelon mosaic virus (MWMV) and three viruses belonging to the genus Carlavirus. The Carlaviruses include cowpea mild mottle virus (CpMMV) and two putative Carlaviruses—closely related but distinct from cucumber vein-clearing virus (CuVCV) with amino acid and nucleotide sequence identities of 75.7–78.1 and 63.6–67.6%, respectively, in the coat protein genes. In reference to typical symptoms observed in the infected plants, the two putative Carlaviruses were named papaya mottle-associated virus (PaMV) and papaya mild mottle-associated virus (PaMMV). Surprisingly, and in contrast to previous studies conducted in other parts of world, PRSV was not detected. The majority of the viruses were detected as single viral infections, while a few were found to be infecting alongside another virus (for example, MWMV and PaMV). Furthermore, the NGS and RT-PCR analysis identified MWMV as being strongly associated with ringspot symptoms in infected papaya fruits. This study has provided the first complete genome sequences of these viruses isolated from papaya in Kenya, together with primers for their detection—thus proving to be an important step towards the design of long-term, sustainable disease management strategies.

Analysis of shotgun metagenomic data generated from next generation sequencing platforms can be done through a variety of bioinformatic pipelines. These pipelines employ different sets of sophisticated bioinformatics algorithms which may affect the results of this analysis. In this study, we compared two commonly used pipelines for shotgun metagenomic analysis: MG‐RAST and Kraken 2, in terms of taxonomic classification, diversity analysis, and usability using their primarily default parameters. Overall, the two pipelines detected similar abundance distributions in the three most abundant taxa Proteobacteria, Firmicutes, and Bacteroidetes. Within bacterial domain, 497 genera were identified by both pipelines, while an additional 694 and 98 genera were solely identified by Kraken 2 and MG‐RAST, respectively. 933 species were detected by the two algorithms. Kraken 2 solely detected 3550 species, while MG‐RAST identified 557 species uniquely. For archaea, Kraken 2 generated 105 and 236 genera and species, respectively, while MG‐RAST detected 60 genera and 88 species. 54 genera and 72 species were commonly detected by the two methods. Kraken 2 had a quicker analysis time (~4 hours) while MG‐RAST took approximately 2 days per sample. This study revealed that Kraken 2 and MG‐RAST generate comparable results and that a reliable high‐level overview of sample is generated irrespective of the pipeline selected. However, Kraken 2 generated a more accurate taxonomic identification given the higher number of “Unclassified” reads in MG‐RAST. The observed variations at the genus level show that a main restriction is using different databases for classification of the metagenomic data. The results of this research indicate that a more inclusive and representative classification of microbiomes may be achieved through creation of the combined pipelines.

Sustainable control of plant diseases requires a good understanding of the epidemiological aspects such as the biology of the causal pathogens. In the current study, we used RT-PCR and Next Generation Sequencing (NGS) to contribute to the characterization of maize lethal necrotic (MLN) viruses and to identify other possible viruses that could represent a future threat in maize production in Tanzania. RT-PCR screening for Maize Chlorotic Mottle Virus (MCMV) detected the virus in the majority (97%) of the samples (n = 223). Analysis of a subset (n = 48) of the samples using NGS-Illumina Miseq detected MCMV and Sugarcane Mosaic Virus (SCMV) at a co-infection of 62%. The analysis further detected Maize streak virus with an 8% incidence in samples where MCMV and SCMV were also detected. In addition, signatures of Maize dwarf mosaic virus, Sorghum mosaic virus, Maize yellow dwarf virus-RMV and Barley yellow dwarf virus were detected with low coverage. Phylogenetic analysis of the viral coat protein showed that isolates of MCMV and SCMV were similar to those previously reported in East Africa and Hebei, China. Besides characterization, we used farmers’ interviews and direct field observations to give insights into MLN status in different agro-ecological zones (AEZs) in Kilimanjaro, Mayara, and Arusha. Through the survey, we showed that the prevalence of MLN differed across regions (P = 0.0012) and villages (P < 0.0001) but not across AEZs (P > 0.05). The study shows changing MLN dynamics in Tanzania and emphasizes the need for regional scientists to utilize farmers’ awareness in managing the disease.