Biology subjects

Okumu, F. O.

Publications and source records attributed to Okumu, F. O..

10 recordsLinked to original sources

bioRxiv2026

How reliable is the use of ovarian dilatations for mosquito age-grading? A blinded multi-rater validation of the Polovodova method in Anopheles mosquitoes.

BackgroundAccurate estimation of mosquito age is critical for malaria surveillance, as only older female Anopheles mosquitoes can transmit the parasite. Traditional age-grading methods, include the Polovodova technique in which dilatations on the ovarian pedicel to tally completed gonotrophic cycles (GC) are counted. Despite their widespread use, these techniques remain poorly validated under realistic operational conditions, with limited evidence on their precision and accuracy across different parity rates, GC, and species. In this study, the accuracy and precision of the Polovodova method was evaluated across multiple age-graders and species under both laboratory and field conditions. MethodsA blinded validation study was conducted using laboratory-reared Anopheles arabiensis and Anopheles funestus (N > 3,600) spanning four gonotrophic cycles, and field-collected mosquitoes from Ulanga district, Tanzania (N = 600). Three blinded researchers performed dissections and readings, while a fourth unblinded researcher served as the reference reader (R0) to obtain ground truth (for parity) or assumed truth (for GC) for laboratory mosquitoes. Accuracy and precision for parity and gonotrophic cycle classification were assessed using pairwise inter-rater comparisons, percent agreement, Fleiss kappa and Cohens kappa O_SCPCAP(C_SCPCAPO_SCPLOWKC_SCPLOWO_SCPCAP)C_SCPCAP statistics. For field mosquitoes, which had no age-referenced group, only precision was assessed. FindingsThe Polovodova method accurately classified parity in both species, correctly identifying 98-100% of nulliparous and 94-96% of parous mosquitoes compared with the reference dataset. Inter-rater reliability for parity was almost perfect in An. arabiensis and An. funestus (k = 0.81-0.83), with 82-96% pairwise agreement. GC classification was highly accurate for early stages (GC0-GC2: 85-100%) but declined at later stages GC4(34-46%), reflecting systematic underestimation of mosquito age. Overall GC reliability was moderate (k = 0.56-0.59). Field classifications showed almost perfect agreement (k = 0.87-0.88), supporting operational applicability. ConclusionsThe Polovodova method provides robust estimates of parity and early gonotrophic-cycle status in Anopheles mosquitoes, but late-cycle classifications are more prone to reader disagreement and systematic underestimation. Operational use should therefore rely on trained multiple-reader workflows, including adjudication of discordant or late-cycle specimens, rather than single-reader classification. These findings provide empirical benchmarks for quality-assured Polovodova age-grading in malaria vector surveillance and intervention evaluation.

ecology↗

bioRxiv2025

Microsatellite Analysis of Genetic Differentiation Among Populations of the Malaria Vector, Anopheles funestus, across Mainland Tanzania Reveals Contrasted Patterns of Geographic Isolation and Gene Flow

BackgroundDespite Anopheles funestus s.s. being a highly competent and widespread malaria vector in Africa, its population structure remains largely understudied in many countries, including Tanzania. Herein, we examine the genetic diversity, geographic isolation, and gene flow of An. funestus populations across 10 administrative regions in mainland Tanzania. MethodsWe employed 12 previously used microsatellite DNA markers to describe genetic diversity, isolation by distance, and gene flow patterns among 10 An. funestus s.s. populations (n = 654) and one An. parensis population (n = 28), used as an outgroup, sampled across 10 regions in mainland Tanzania. ResultsOverall, allelic richness (Na) and genetic diversity (HS) did not differ significantly among populations. Although some loci and populations showed significant departures from Hardy-Weinberg equilibrium, the patterns were not indicative of sub-structuring within locations. Pairwise genetic divergence (FST) values indicated clear separation between An. parensis and An. funestus s.s., with values exceeding 0.2, consistent with species-level differentiation. Among An. funestus s.s. populations, the highest divergence was observed between southeastern coastal populations (Mtwara, Ruvuma, Lindi) and inland populations, with FST values up to 0.288. There was no evidence of isolation by distance. Instead, patterns of genetic divergence suggested connectivity across the Rift Valley and heterogeneity among southeastern populations. Neighbour-joining analysis and Bayesian genotype clustering identified three distinct population groups: (i) An. parensis (Dodoma), (ii) a genetically distinct An. funestus s.s. population from Mtwara, and (iii) a more homogeneous cluster comprising the remaining An. funestus s.s. populations. Notably, the Mtwara population appeared highly differentiated, with divergence approaching that between An. Funestus s.s. and An. parensis, supporting its distinctiveness but not undermining the role of An. parensis as an outgroup. ConclusionExcept for the Mtwara population, whose status will need to be clarified through whole genome sequencing, moderate genetic divergence was found among An. funestus s.s. populations across Tanzania, despite geographical separation and the Rift Valley. The observed genetic structure suggests that anthropogenic gene flow may play a key role in shaping population divergence. Future studies should aim to delineate the effects of local adaptation from recent gene flow to further explore these dynamics.

genetics↗

bioRxiv2025

Characterization of a new laboratory colony of Anopheles funestus mosquitoes established in Ifakara, Tanzania

BackgroundAnopheles funestus, a major vector of malaria in Africa, has proven difficult to colonize in laboratory settings, impeding research on its biology and control. After several attempts, our team recently succeeded in colonizing a strain of An. funestus from Tanzania (FUTAZ). The objective of this study was to analyse the key fitness and genotypic characteristics of these mosquitoes during multiple filial generations of laboratory adaptation and compare them to wild An. funestus from Tanzania and a pre-existing colony of An. funestus from Mozambique (FUMOZ). MethodsMeasures of mating success (percentage of female mosquitoes inseminated), body size (wing length), fecundity (number of eggs laid per female), and insecticide susceptibility (percentage of 24-hour mortality after exposure to insecticides) were compared between the newly established colonies of Tanzanian An. funestus (FUTAZ colonies), the long-established FUMOZ colonies, and a colony of Anopheles arabiensis maintained in the same laboratory. The maternal lineages of the An. funestus mosquitoes were investigated through a hydrolysis probe analysis of their mitochondrial DNA to identify distinct clades, I and II. Additionally, other intragenomic variations were examined through a PCR analysis of restriction fragment length polymorphisms (RFLP) on the third domain of 28S ribosomal DNA. These molecular markers were used to compare the FUTAZ colonies, FUMOZ colonies in Tanzania and South Africa, and the wild-collected An. funestus from Tanzania. ResultThe mating success and body size of FUTAZ females declined significantly from filial generations F1 to F6 relative to the founder population (F0), but then increased from F7 onwards eventually matching FUMOZ by F9. Fecundity was similar across all colonies tested. However, it took significantly longer for 50% of the females in the FUTAZ and FUMOZ colonies (over 10 days) to mate compared to females in the An. arabiensis colony (approximately 5 days). Insecticide resistance appeared to be lost during colonization, but this varied with insecticide classes. Majority of mosquitoes in the FUTAZ colony, as well as the wild-caught Tanzanian An. funestus belonged to Clade I (80.4-89.4%) and RFLP type "Y" (90.5-91.4%), while the FUMOZ colonies were mostly Clade II (65.5-88.5%) and RFLP type "MW" (90.5-91.5%). ConclusionThis study suggests that the mating success and body size of An. funestus decreases significantly during the early stages of colonization, then increase as the mosquitoes adapt to laboratory conditions. It is therefore crucial to have a large enough founder population to persist through these early generations in order to achieve stable colonization of An. funestus. The Clade and RFLP genotyping demonstrated the genetic similarities between the FUTAZ mosquitoes and wild-caught Tanzanian An. funestus, but also showed that the new colony can be distinguished from the FUMOZ colony.

ecology↗

bioRxiv2025

Delayed copulation and mating in the malaria vector Anopheles funestus compared to Anopheles arabiensis

Mating is a vital behavior for mosquito reproduction, yet it remains poorly understood under captive conditions. We examined the copulation dynamics of two key malaria vectors, Anopheles funestus, and Anopheles arabiensis, in controlled laboratory settings in Tanzania. We observed how variations in mosquito age and artificial lighting influence mating success for these two mosquito species within cages under controlled conditions. We conducted observations in 24-hour cycles, monitoring copulation events and insemination in females. We used generalized linear mixed models (GLMMs) for statistical analyses to assess how environmental conditions influence mating behavior. We found that Anopheles arabiensis exhibited rapid copulation, with 32.4% of individuals mating by Day 3 post-emergence, while An. funestus showed delayed activity, reaching a similar mating rate by Day 8. The introduction of artificial red light significantly accelerated copulation in An. funestus but did not affect An. arabiensis. Dissection confirmed successful sperm transfer and mating plug delivery in over 92% of copulating pairs for both species. Mating occurred primarily at night, with distinct peaks at 22:00 for An. arabiensis and 23:00 for An. funestus. In conclusion, our findings reveal species-specific differences in reproductive behavior, which could improve the colonization of An. funestus, a species historically challenging to rear in captivity. These insights may also inform the development of new vector control technologies, such as sterile insect techniques and genetic-based approaches, that exploit mosquito mating behaviors.

ecology↗

bioRxiv2025

Distinct Genetic Populations and Resistance Backgrounds of the Malaria Vector Anopheles funestus in Tanzania

Population genetic analysis of mosquitoes is becoming increasingly important for understanding the distribution of insecticide resistance alleles, devising sustainable insecticide-based vector control approaches, and how malaria vector populations are structured in space. Anopheles funestus is the dominant malaria vector in Tanzania and most parts of East and Southern Africa. To better understand its population genomic structure in Tanzania, we sequenced the genomes of 334 individual An. funestus mosquitoes from 11 administrative regions. We found two genetically differentiated populations; one inland and at high altitude (found in Katavi, Kagera, Kigoma, and Mwanza) and a second coastal, at low altitude (found in Pwani, Morogoro, Tanga, Ruvuma, Mtwara, Dodoma, and Lindi), with differences in genetic diversity and inbreeding. We found asynchronous selective sweeps, associated with insecticide resistance phenotypes, at the Cyp9k1 gene, and Cyp6p gene cluster, with distinct copy number-variant profiles between the coastal and inland populations. These results suggest that inland and coastal An. funestus populations have divergent histories, with the arid, central region of Tanzania, which also contains the Rift Valley being a possible barrier to gene flow. Such population disconnectedness should be considered for insecticide deployment, resistance management, and the rollout of novel genetic- based vector control approaches. These findings provide the most detailed study of Tanzanian An. funestus population structure and resistance genetics to date. Future research should examine the epidemiological relevance of this discontinuity in gene flow and whether these populations have different malaria transmission abilities.

genetics↗

bioRxiv2024

Discovery of knock-down resistance in the major malaria vector Anopheles funestus reveals the legacy of persistent DDT pollution.

A major mechanism of insecticide resistance in insect pests is knock-down resistance (kdr) caused by mutations in the voltage-gated sodium channel (Vgsc) gene. Despite being common in most malaria Anopheles vector species, kdr mutations have never been observed in Anopheles funestus, the principal malaria vector in Eastern and Southern Africa. While monitoring 10 populations of An. funestus in Tanzania, we unexpectedly found resistance to DDT, a banned insecticide, in one location. Through whole-genome sequencing of 333 An. funestus samples from these populations, we found 8 novel amino acid substitutions in the Vgsc gene, including the kdr variant, L976F (L1014F in An. gambiae), in tight linkage disequilibrium with another (P1842S). The mutants were found only at high frequency in one region, with a significant decline between 2017 and 2023. Notably, kdr L976F was strongly associated with survivorship to the exposure to DDT insecticide, while no clear association was noted with a pyrethroid insecticide (deltamethrin). Further study is necessary to identify the origin and spread of kdr in An. funestus, and the potential threat to current insecticide-based vector control in Africa. SignificanceKnock-down resistance (kdr) mutations confer resistance to malaria vector control insecticides and pose a grave threat to malaria control. Here, we report the first discovery of kdr in An. funestus, the principal malaria vector in East and Southern Africa. Kdr in An. funestus conferred resistance to DDT but not deltamethrin. Based on extensive DDT contamination and unofficial usage in Tanzania, it is possible that kdr emerged because of widespread organic pollution as opposed to through public health efforts. Regardless of origin, the discovery of kdr in An. funestus is an alarming development that warrants immediate, extensive follow-up and close surveillance to establish the origin, and extent to which it may threaten malaria control in An. funestus.

genetics↗

bioRxiv2022

Using transfer learning and dimensionality reduction techniques to improve generalisability of machine-learning predictions of mosquito ages from mid-infrared spectra

Accurate prediction of mosquito population age structures can improve the evaluation of mosquito-targeted interventions since old mosquitoes are more likely to transmit malaria than young ones. Mid-infrared spectroscopy (MIRS) reveals age-associated variation in the biochemical composition of the mosquito cuticle, which can then be used to train machine learning (ML) models to predict mosquito ages. However, these MIRS-ML models are not always generalisable across different mosquito populations. Here, we investigated whether dimensionality reduction applied to the MIRS input data and transfer learning could improve the generalisability of MIRS-ML predictions for mosquito ages. We reared adults of the malaria vector, Anopheles arabiensis, in two insectaries (Ifakara, Tanzania and Glasgow, UK). The heads and thoraces of female mosquitoes of two age classes (1-9 day-olds and 10-17 day-olds) were scanned using an attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectrometer (4000 cm-1 to 400 cm-1). The dimensionality of the spectra data was reduced using unsupervised principal component analysis (PCA) or t-distributed stochastic neighbour embedding (t-SNE), and then the spectra were used to train deep learning (DL) and standard machine learning (ML) classifiers. Transfer learning was also evaluated for improving the computational cost of the models when predicting mosquito age classes from new populations. Model accuracies for predicting the age of test mosquitoes from the same insectary as the training samples reached 99% for DL and 92% for ML, but did not generalise to a different insectary, achieving only 46% and 48% for ML for DL, respectively. Dimensionality reduction did not improve the model generalisability between locations but reduced computational time up to 5-fold. However, transfer learning by updating pre-trained models with 2% of mosquitoes from the alternate location brought both DL and standard ML model performance to ~98% accuracy for predicting mosquito age classes in the alternative insectary. Combining dimensionality reduction and transfer learning can reduce computational costs and improve the transferability of both deep learning and standard machine learning models for predicting the age of mosquitoes. Future studies could investigate the optimal quantities and diversity of training data necessary for transfer learning, and implications for broader generalisability to unseen datasets.

bioinformatics↗

bioRxiv2021

Sub-lethal aquatic doses of pyriproxyfen may increase pyrethroid resistance in malaria mosquitoes

BackgroundPyriproxyfen (PPF), an insect growth hormone mimic is widely used as a larvicide and in some second-generation bed nets, where it is combined with pyrethroids to improve impact. It has also been evaluated as a candidate for auto-dissemination by adult mosquitoes to control Aedes and Anopheles species. We examined whether PPF added to larval habitats of pyrethroid-resistant malaria vectors can modulate levels of resistance among emergent adult mosquitoes. MethodologyThird-instar larvae of pyrethroid-resistant Anopheles arabiensis (both laboratory-reared and field-collected) were reared in different PPF concentrations, between 1x10-9 milligrams active ingredient per litre of water (mgAI/L) and 1x10-4 mgAI/L, or no PPF at all. Emergent adults escaping these sub-lethal exposures were tested using WHO-standard susceptibility assays on pyrethroids (0.75% permethrin and 0.05% deltamethrin), carbamates (0.1% bendiocarb) and organochlorides (4% DDT). Biochemical basis of pyrethroid resistance was investigated by pre-exposure to 4% PBO. Bio-efficacies of long-lasting insecticide-treated nets, Olyset(R) and PermaNet 2.0 were also examined against adult mosquitoes with or without previous aquatic exposure to PPF. ResultsAddition of sub-lethal doses of PPF to larval habitats of pyrethroid-resistant An. arabiensis, consistently resulted in significantly reduced mortalities of emergent adults when exposed to pyrethroids, but not to bendiocarb or DDT. Mortality rates after exposure to Olyset(R) nets, but not PermaNet 2.0 were also reduced following aquatic exposures to PPF. Pre-exposure to PBO followed by permethrin or deltamethrin resulted in significant increases in mortality, compared to either insecticide alone. ConclusionsPartially-resistant mosquitoes exposed to sub-lethal aquatic concentrations of PPF may become more resistant to pyrethroids than they already are without such pre-exposures. Studies should be conducted to examine whether field applications of PPF, either by larviciding or other means actually excercebates pyrethroid-resistance in areas where signs of such resistance already exist in wild the vector populations. The studies should also investigate mechanisms underlying such magnification of resistance, and how this may impact the potential of PPF-based interventions in areas with pyrethroid resistance.

ecology↗

bioRxiv2020

Rapid ageing and species identification of natural mosquitoes for malaria surveillance

The malaria parasite, which is transmitted by several Anopheles mosquito species, requires more time to reach its human-transmissible stage than the average lifespan of a mosquito. Monitoring the species-specific age structure of mosquito populations is critical to evaluating the impact of vector control interventions on malaria risk. We developed a rapid, cost-effective surveillance method based on deep learning of mid-infrared spectra of mosquitoes cuticle that simultaneously identifies the species and the age of three main malaria vectors, in natural populations. Using over 40,000 ecologically and genetically diverse females, we could speciate and age grade An. gambiae, An. arabiensis, and An. coluzzii with up to 95% accuracy. Further, our model learned the age of new populations with minimal sampling effort and detected the impact of control interventions on simulated mosquito populations, measured as a shift in their age structures. We anticipate our method to be applied to other arthropod vector-borne diseases.

zoology↗

bioRxiv2018

Prediction of malaria mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning

Despite the global efforts made in the fight against malaria, the disease is resurging. One of the main causes is the resistance that Anopheles mosquitoes, vectors of the disease, have developed to insecticides. Anopheles must survive for at least 12 days to possibly transmit malaria. Therefore, to evaluate and improve malaria vector control interventions, it is imperative to monitor and accurately estimate the age distribution of mosquito populations as well as total population sizes. However, estimating mosquito age is currently a slow, imprecise, and labour-intensive process that can only distinguish under-from over-four-day-old female mosquitoes. Here, we demonstrate a machine-learning based approach that utilizes mid-infrared spectra of mosquitoes to characterize simultaneously, and with unprecedented accuracy, both age and species identity of females of the malaria vectors Anopheles gambiae and An. arabiensis mosquitoes within their respective populations. The prediction of the age structures was statistically indistinguishable from true modelled distributions. The method has a negligible cost per mosquito, does not require highly trained personnel, is substantially faster than current techniques, and so can be easily applied in both laboratory and field settings. Our results show that, with larger mid-infrared spectroscopy data sets, this technique can be further improved and expanded to vectors of other diseases such as Zika and Dengue.

bioinformatics↗