Biology subjects

Manica, M.

Publications and source records attributed to Manica, M..

7 recordsLinked to original sources

bioRxiv2026

End-of-outbreak determination under seasonal transmission

At the apparent end of an infectious disease outbreak, policymakers must decide when to relax interventions, balancing epidemiological safety and economic efficiency. Here, we extend a renewal equation framework for estimating the risk of additional cases to scenarios with seasonal transmission, such as Aedes-borne virus transmission in temperate settings. In simulations, we demonstrate that the time of year is key to when it is safe to relax interventions under seasonal transmission: measures can be relaxed sooner if the last observed case occurs towards the end of the transmission season. We apply our framework to the 2017 chikungunya outbreak in Italy, integrating incidence data with estimates of temperature suitability for transmission, and further extend our approach to account for case under-reporting. Our model incorporating an end-of-season decline in transmission indicates a lower risk of additional cases late in the outbreak than a model based on incidence alone: assuming 60% case reporting, the seasonal model reaches a 1% risk threshold 22 days after the last observed case, compared with 39 days for the incidence-only model. Incorporating seasonality in model-based assessments of transmission risks at the end of an outbreak can therefore support more timely and reliable decision making.

ecology↗

bioRxiv2025

Evaluating Field Trial Designs for Genetically Modified Mosquito Interventions: An In-Silico Simulation Approach

Mosquito control strategies based on the mass release of modified males, such as genetically modified mosquitoes (GMMs), aim to suppress wild populations by impairing reproduction. Evaluating these interventions requires resource-intensive field trials, but a lack of standardized implementation practices, particularly regarding release ratios of modified males to wild female mosquitoes and trial timing, has led to variable outcomes. This studys objective is to propose a modeling tool for the "in-silico" simulation of trial designs before field implementation. To this aim, we developed an agent-based model of mosquito population dynamics. As a case study, we calibrated the model using 2019-2023 Aedes aegypti surveillance data from Miami-Dade County, Florida, and compared two GMM trial designs as illustrative examples. Our results show that depending on the implementation choices (e.g., trial start date and duration, release ratio), trials yield highly variable outcomes. For example, changing the start date while fixing all other implementation details can lead to effectiveness between 50% and 90%. Our findings suggest that "in-silico" simulation is a valuable tool for improving trial protocol design, allowing stakeholders to test strategies and reduce outcome uncertainty before committing to a fieldwork experiment.

ecology↗

bioRxiv2024

A language model assistant for biocatalysis

Language model assistants have transformed how researchers interact with computational tools, offering unprecedented capabilities in understanding and generating complex scientific queries. We introduce a language model assistant for biocatalysis (LM-ABC), a computational tool designed to streamline workflows in enzyme engineering research. LM-ABC integrates a large language model with domain-specific modules to facilitate biocatalysis research through natural language inputs. Its architecture employs the Reasoning and Acting (ReACT) framework for dynamic tool selection and chaining, enabling functionalities like binding site extraction and molecular dynamics simulations. LM-ABC can interpret and process user queries in the form of natural language, and interface with existing computational resources to generate relevant results for enzyme engineering. Additionally, LM-ABC is available via both command-line and web-based interfaces, which lowers the barriers for its usage and integration in various disciplines. Provided as open-source software, the LM-ABC contributes to the application of language models in computational biology, potentially accelerating enzyme engineering research processes.

bioengineering↗

bioRxiv2021

Aedes koreicus, a vector on the rise: pan-European genetic patterns, mitochondrial and draft genome sequencing

BackgroundThe mosquito Aedes koreicus (Edwards, 1917) is a recent invader on the European continent that was introduced to several new places since its first detection in 2008. Compared to other exotic Aedes mosquitoes with public health significance that invaded Europe during the last decades, this species biology, behavior, and dispersal patterns were poorly investigated to date. Methodology/Principal FindingsTo understand the species population relationships and dispersal patterns within Europe, a fragment of the COI gene was sequenced from 130 mosquitoes, collected from five countries where the species has been introduced and/or established. Oxford Nanopore and Illumina sequencing techniques were combined to generate the first complete nuclear and mitochondrial genomic sequences of Ae. koreicus from the European region. The complete genome of Ae. koreicus is 879 Mb. COI haplotype analyses identified five major groups (altogether 31 different haplotypes) and revealed a large-scale dispersal pattern between European Ae. koreicus populations. Continuous admixture of populations from Belgium, Italy, and Hungary was highlighted, additionally, haplotype diversity and clustering clearly indicate a separation of German sequences from other populations, pointing to an independent introduction of Ae. koreicus to Europe. Finally, a genetic expansion signal was identified, suggesting the species might be present in more locations than currently detected. Conclusions/SignificanceOur results highlight the importance of genetic research of invasive mosquitoes to understand general dispersal patterns, reveal main dispersal routes and form the baseline of future mitigation actions. The first complete genomic sequence also provides a significant leap in the general understanding of this species, opening the possibility for future genome-related studies, such as the detection of Single Nucleotide Polymorphism markers. Considering its public health importance, it is crucial to further investigate the species population genetic dynamic, including a larger sampling and additional genomic markers. Author SummaryIn the present context of globalization and changing environment, the rapid spread of Invasive Mosquito Species (IMS) across Europe represents a serious public health threat because some species are competent vectors for several pathogens. A better knowledge of the IMS population relationships, demographic trends, and dispersal patterns can help the relevant authorities mitigating further spread. Aedes koreicus is an IMS that invaded the continent and has been expanding its geographic range over the last decade. In the present study, one of the most popular DNA marker (COI) was used to investigate the pan-European haplotype diversity and phylogenetic relatedness within and between Ae. koreicus populations. Also, the first complete mitochondrial genome and draft nuclear genome of Ae. koreicus were generated using combined high-throughput sequencing techniques (Oxford Nanopore, Illumina). This provides a significant leap in the general understanding of this species and opens the possibility for future genomic studies.

genetics↗

bioRxiv2021

Towards a systematic characterization of protein complex function: a natural language processing and machine-learning framework

It is a general assumption of molecular biology that the ensemble of expressed molecules, their activities and interactions determine biological processes, cellular states and phenotypes. Quantitative abundance of transcripts, proteins and metabolites are now routinely measured with considerable depth via an array of "OMICS" technologies, and recently a number of methods have also been introduced for the parallel analysis of the abundance, subunit composition and cell state specific changes of protein complexes. In comparison to the measurement of the molecular entities in a cell, the determination of their function remains experimentally challenging and labor-intensive. This holds particularly true for determining the function of protein complexes, which constitute the core functional assemblies of the cell. Therefore, the tremendous progress in multi-layer molecular profiling has been slow to translate into increased functional understanding of biological processes, cellular states and phenotypes. In this study we describe PCfun, a computational framework for the systematic annotation of protein complex function using Gene Ontology (GO) terms. This work is built upon the use of word embedding-- natural language text embedded into continuous vector space that preserves semantic relationships-- generated from the machine reading of 1 million open access PubMed Central articles. PCfun leverages the embedding for rapid annotation of protein complex function by integrating two approaches: (1) an unsupervised approach that obtains the nearest neighbor (NN) GO term word vectors for a protein complex query vector, and (2) a supervised approach using Random Forest (RF) models trained specifically for recovering the GO terms of protein complex queries described in the CORUM protein complex database. PCfun consolidates both approaches by performing the statistical test for the enrichment of the top NN GO terms within the child terms of the predicted GO terms by RF models. Thus, PCfun amalgamates information learned from the gold-standard protein-complex database, CORUM, with the unbiased predictions obtained directly from the word embedding, thereby enabling PCfun to identify the potential functions of putative protein complexes. The documentation and examples of the PCfun package are available at https://github.com/sharmavaruns/PCfun. We anticipate that PCfun will serve as a useful tool and novel paradigm for the large-scale characterization of protein complex function.

bioinformatics↗

bioRxiv2021

Aedes albopictus bionomics in Procida Island, a promising Mediterranean site for the assessment of innovative and community-based integrated pest management methods.

The colonization of Mediterranean Europe and of other temperate regions by Aedes albopictus created in the last decades an unprecedented nuisance problem in highly infested areas, as well as a new public health threat due to the species competence to transmit exotic arboviruses, such as dengue, chikungunya and zika. The Sterile Insect Technique (SIT) and the Incompatible Insect Technique (IIT) are insecticide-free mosquito-control methods relying on mass release of irradiated/manipulated males which are believed to have a potential in complementing existing and only partially effective control tools. Testing and implementing these approaches are challenging and selection of study sites is an instrumental and crucial step. We carried out a 4-year study in Procida Island (Gulf of Naples, Italy) in strict collaboration with local administrators and citizens to estimate: i) the temporal dynamics, spatial distribution, and population size of Ae. albopictus; and ii) the dispersal and survival of irradiated males. Overall, results provide insights on the bionomics of the mosquito in Southern Europe and draw attention to Procida Island as an ideal site to test innovative control programs against Ae. albopictus which may be used in other Mediterranean and temperate areas."

ecology↗

bioRxiv2020

Proteogenomic heterogeneity of localized human prostate cancer progression

BackgroundTumor-specific genomic aberrations are routinely determined by high throughput genomic measurements. It remains unclear though, how complex genome alterations affect molecular networks through changing protein levels, and consequently biochemical states of tumor tissues. ResultsHere, we investigated the propagation of genomic effects along the axis of gene expression during prostate cancer progression. For that, we quantified genomic, transcriptomic and proteomic alterations based on 105 prostate samples, consisting of benign prostatic hyperplasia regions and malignant tumors, from 39 prostate cancer patients. Our analysis revealed convergent effects of distinct copy number alterations impacting on common downstream proteins, which are important for establishing the tumor phenotype. We devised a network-based approach that integrates perturbations across different molecular layers, which identified a sub-network consisting of nine genes whose joint activity positively correlated with increasingly aggressive tumor phenotypes and was predictive of recurrence-free survival. Further, our data revealed a wide spectrum of intra-patient network effects, ranging from similar to very distinct alterations on different molecular layers. ConclusionsThis study uncovered molecular networks with remarkably convergent alterations across tumor sites and patients, but it also exposed a diversity of network effects: we could not identify a single sub-network that was perturbed in all high-grade tumor regions.

systems biology↗