Lassa virus is a member of the Arenaviridae family, a major cause of viral hemorrhagic fever. This virus is associated with severe neurological complications in a select group of patients. The evolutionary mechanisms behind genetic diversity, its adaptation, and its potential neuropathogenicity are still poorly understood. In this study, a comprehensive evolutionary analysis of the S and L genomes of Lassavirus was conducted, with an emphasis on ancestral reconstruction and genomic structure, as well as the identification of mutations that may contribute to viral adaptation leading to neurological disease. A set of complete S and L genomes was collected from NCBI. These sequences were carefully aligned using the MUSCLE algorithm to ensure a high match at each site. GTR+Gamma was used for evolutionary inference and was selected based on a statistical model test for its ability to accurately reflect the evolutionary dynamics of Lassa virus genomes. Phylogenetic trees were constructed using the maximum-likelihood algorithm RAxML, followed by careful preliminary analyses to assess the reliability of each branch in the tree. The mutations and recombination at the ancestral node were identified, which is likely a crucial point in the virus's ability to adapt and evolve. The emergence and distribution of major mutations across the viral lineage can be monitored. Notably, strains linked to known neurological problems frequently exhibit mutations, suggesting a possible link between certain genetic alterations and LASV's neuroinvasive characteristics. Our outcomes shed light on how genetic variety in the S and L segments impacts neurotropic virulence and offer important new insights into the evolutionary history and genomic adaptability of LASV. In order to anticipate neurological risk, create centered diagnostics, and direct the establishment of medical methods against neurotropic arenavirus infection, this study sets up the basis for future research.
With exponentially increasing genomic and biological data, bioinformatics has emerged as an enabling science of this era, particularly in medicine, genetics, microbiology, and drug discovery. Bioinformatics refers to the application of computer technology for analysing biological data like DNA, RNA, or protein sequences. It enables researchers to manipulate, compare, and analyse vast genomic databases; identify mutations, gene functions, and evolutionary relationships; build phylogenetic trees; reconstruct ancestral sequences; and accelerate drug target discovery and disease gene mapping. Bioinformatics places the scale and complexity of biological science today into manageable parameters [1].
Based on the structure of their nucleic acid genomes, viruses are generally divided into DNA or RNA viruses. DNA viruses had more host specificity and phylogenetic similarity in their genomic sequences compared to RNA viruses. The Arenaviridae family of RNA viruses has varied host species and genomic structures [2].
The nomenclature "Arenavirus" originates from the Latin terms "arenosus," meaning "sandy," and "arena," signifying "sand," due to the "sandy" shape of Arenavirus particles seen under an electron microscope. An arenavirus genome comprises two, and occasionally three, single-stranded RNA segments designated as short (S), medium (M), and large (L) [3].
Zoonotic transmission of certain pathogenic marine viruses to human beings on contact with infected animal cadavers, feces, or material infested with them can cause risky and sometimes fatal diseases with hemorrhagic or neurological features, but natural infection in their hosts is typically asymptomatic [4].
RNA-virus-like pathogens that can cause life-threatening and severe diseases in human beings include viruses of the Arenaviridae family. The most famous of these viruses is viral hemorrhagic fever. This family is geographically divided into Old World viruses (such as Lassa) and New World viruses (such as Junin and Machupo). Lassa virus is considered a major challenge to global public health, as it has caused hundreds of thousands of infections and thousands of deaths [5]. Despite this deadly threat, there is no widely licensed vaccine yet. While earlier studies have mapped out how different Lassa virus strains are related, they only looked at one part of the virus's genetic material (either S or L), missing the possible effects of mixing different parts. This oversight could bias the conclusions, leading to incorrect conclusions about the virus's evolutionary relationships or its history [6].
In addition, the identity of the specific genetic mutations that occurred in the past and enabled the virus to adapt to prevailing conditions and spread remains largely unknown [7], [8]. Ancestral reconstruction techniques are useful because they allow us to look back in time at the molecular level [9]. With the help of these powerful computational tools, the most likely ancestral genome sequences of the virus can be inferred from its historical divergence using statistical and evolutionary methods [10].
Recent studies show that Lassa virus infection expands beyond hemorrhagic and systemic symptoms, which could result in major brain damage in certain patients, involving encephalitis, visual problems, and lasting disorders of the brain [11]. Knowing the familial roots of these neurological illnesses is an essential step to developing suitable diagnostic and medical methods. Analyzing the genomic evolution of the virus, including mutation examination, single-nucleotide polymorphisms (SNPs), and genomic recombination sequences, is necessary for understanding how the virus has adapted to gain new characteristics, including the ability to enter the nervous system.
This research presents the gathering and study of complete genomes of the S and L segments of the Lassa virus from global databases, using modern computational and analytical techniques, involving multiple alignment, optimal gene substitution model selection, phylogeny tree construction, and genomic information ancestry reconstruction. The current research aims to clarify genetic changes and the history of the evolution of the virus while studying the potential link between these changes and the onset of neurological challenges in people with the virus. The conclusions of this research project add immensely to our knowledge of neuroviral development and may contribute to the detection, management, and avoidance of viral diseases that impact the body's nervous system.
This research aims to improve our understanding by creating evolutionary trees for the genomes of the two groups (L and S) using the maximum likelihood algorithm, and the following sections will describe how the oldest common ancestor of the virus genomes was built.
Complete S and L segment genomes of Lassa virus were obtained from NCBI, with duplicates and incomplete entries removed [12]. Multiple sequence alignment was performed using MUSCLE, and the best-fit substitution model (GTR+Gamma) was selected. Phylogenetic trees were reconstructed with the maximum-likelihood method in RAxML, supported by bootstrap analysis. Single nucleotide polymorphisms (SNPs) and mutations were identified by comparative alignment, and ancestral genomes were reconstructed using RAxML-NG to infer evolutionary trends. Full technical details (software parameters, command-line instructions, preprocessing scripts) are available in the Supplementary Methods. This section delineates a series of fundamental research steps, each deemed crucial and essential for attaining optimal outcomes. The following sections will explain the comprehensive steps involved in the research process, as illustrated in Fig. 1.
 
                            Fig. 1: Workflow of the bioinformatics analysis. Overview of the computational steps used to analyze Lassa virus genomes, from data selection and alignment to phylogenetic reconstruction and SNP/mutation analysis.
                            Data Selection 
                            The data used in this paper, which are specific to the S and L segments of Lassa virus
                            genomes, were obtained,
                            selected, and refined from the National Center for Biotechnology Information (NCBI) website
                            [12]. These genomes
                            are considered complete genomes, not fragments of genomes. After the selection process was
                            completed, the
                            genomes were processed to eliminate duplicates and genomes containing ambiguous characters
                            (usually designated
                            as "N"), which could be more than 5%. At the end of this stage, we had two sets of
                            complete genomes
                            for the S and L segments. The next stage, considered an important and necessary step for
                            constructing reliable,
                            rooted genome trees, involved selecting a genome from outside the Arenaviridae family but
                            evolutionarily close
                            to it. This genome is called an out-group. In this work, an out-group genome was selected
                            for each segment (with
                            the S segment, JO4324.1 was selected, and with the L segment, the out-group NC_004291.1 was
                            selected).
                        
                            Multiple Sequence Alignment 
                            One of the most fundamental steps in our work is alignment, as it arranges the complete
                            genetic sequences and
                            adjusts them to the same length. This process is performed by adding gaps between the
                            sequences of the viral
                            family's genomes [13]. It is considered crucial and essential in bioinformatics for
                            analyzing genomes and
                            identifying specific differences. The results of the alignment process are used to construct
                            reliable genomic
                            trees.
                            
                            It is implemented using a program called Muscle, which consists of several basic steps:
                            progressive alignment of
                            the draft, adjustment of the alignment, and an additional step, which is repeating the
                            stages to represent and
                            determine the best alignment to adopt [14]. In this research, a set of colors was used for
                            each nucleotide to
                            facilitate understanding and to clarify the differences between the genomes during
                            alignment, as shown in Fig.
                            2, which includes a and b and illustrates part of the sequence arrangement for the S and L
                            segment genomes.
                        
 
                            Fig. 2: Multiple sequence alignment of Lassa virus genomes. Aligned sequences of the (a) S and (b) L segments reveal conserved regions and mutational hotspots. These differences form the basis for identifying variants that may contribute to neurotropism.
                            Synteny 
                            One of the important tools that helps us understand and identify important information about
                            the structure of
                            the genome is synteny. It is a big part of genomic research [15]. This step comes after
                            aligning several genomes
                            and involves finding conserved gene configurations, looking into genome duplications, and
                            studying chromosomal
                            rearrangements. When choosing genomes and doing synteny alignment, the presence of only a
                            few common areas shows
                            that the genomes are very different, so they should not be included [16, 17]. Look at Fig. 3
                            (a, b), which
                            displays the synteny of several sequences from the Lassa S and L segment genomes by counting
                            how many genes are
                            kept in the same order. Blue indicates the least similarity, and red indicates the most
                            similarity. In Fig. 4
                            (a, b), these numbers indicate the percentage of similarity between the genomes when
                            compared.
                        
 
                            Fig. 3: Genome synteny of Lassa virus sequences. Synteny plots for the (a) S and (b) L segments highlight conserved and divergent regions across genomes, suggesting possible adaptations relevant to host interactions.
 
                            Fig. 4: Synteny heatmaps of Lassa virus genomes. Heatmaps for the (a) S and (b) L segments show degrees of similarity across viral strains. Regions of low similarity may indicate mutational hotspots linked to altered virulence.
                            Building Phylogenetic Trees 
                            Molecular phylogeny is employed to examine the links among a collection of entities by
                            constructing a phylogenetic or evolutionary tree. The
                            history of evolution found in genomes shows patterns like a tree when the right data, models
                            for changes, and
                            methods for building the tree are used. These evolutionary patterns are employed to examine
                            the connections
                            among the entities [18, 19].
                            
                            Before constructing the trees, a model appropriate for the genomic data was selected. This
                            model was GTR+G. The
                            genomic trees were then constructed using the maximum likelihood algorithm for both
                            segments, which is an
                            advanced statistical method. This tree is used to visualise and demonstrate the
                            relationships between the
                            genomes used. The primary benefit of these trees is to understand and illustrate how genomes
                            or species diverged
                            from a common ancestor over time. The method is powerful because it uses all available
                            genomes and an
                            appropriate evolutionary model, but it requires significant computing power [20].
                        
                            Maximum Likelihood algorithm 
                            Understanding evolutionary connections among species is essential for several biological
                            research projects. A
                            clear phylogenetic tree is crucial for understanding important changes in evolution and is
                            necessary for
                            figuring out where new genes come from, spotting molecular changes, explaining how physical
                            traits have evolved,
                            and reconstructing population changes in species that have recently split apart [21, 22].
                            Despite the increasing
                            abundance of data and the availability of robust analytical methodologies, several problems
                            persist in achieving
                            trustworthy tree construction [23].
                            
                            This algorithm builds the base tree using a powerful and important program called RAxML,
                            along with a search
                            that includes the best tree [24]. Lassa virus genome segments S and L were reconstructed by
                            the Maximum
                            Likelihood method implemented in RAxML v8.2.12 [25]. Viral genomes were presented in FASTA
                            format, and
                            preliminary data processing, including sequence parsing, quality filtering, and formatting,
                            was performed using
                            Python, extensively utilising the Biopython package for modules to handle input/output on
                            sequences and
                            alignments: Bio.SeqIO and Bio.AlignIO.
                            
                            We used the standalone MUSCLE tool within Python to perform multiple sequence alignment
                            (MSA), allowing us to
                            repeat and automate the procedure. The genome sequences were produced in PHYLIP format so
                            that RAxML could read
                            them. Phylogenetic trees were estimated with the GTR+G (General Time Reversible with Gamma
                            distribution)
                            substitution model, which can handle rate variation across nucleotide sites and is a better
                            representation of
                            evolutionary processes. We carried out a bootstrap analysis with 1000 repeats to evaluate
                            the statistical
                            support of the topology of the resulting tree, as bootstrap analyses are commonly used to
                            estimate the
                            robustness of inferred clades.
                            
                            The most crucial command executed by RAxML was: (raxmlHPC s aligned.phy -n
                            output_tree -m GTRGAMMA -p
                            12345 -x 12345 -# 1000 -f a),Where s is the aligned input file in PHYLIP format, n gives the
                            name for the output
                            file, m GTRGAMMA chooses the GTR substitution model with gamma-distributed rate variation, p
                            and -x assign
                            random seeds for bootstrapping and tree construction, 1000 indicates the number of bootstrap
                            replicates, and (f,
                            a) allows a rapid bootstrap analysis with the discovery of the most optimal maximum
                            likelihood tree. After tree
                            reconstruction, representative final phylogenetic trees were displayed using FigTree v1.4.4.
                            Clades
                            were colored or named according to their geographic or historical aspects, and
                            branches were indicated
                            by bootstrap support values. This helped people better understand evolution.
                            
                            A complex biological signal can be obtained from the tree of phylogeny produced in
                            this investigation,
                            which illustrates the evolutionary links between different Lassa virus strains. This signal
                            offers a rich
                            dataset for computational modeling, as it is expressed using branching patterns and
                            bootstrap support values.
                            These properties may be used to train algorithms that categorize viral variations, forecast
                            future evolutionary
                            trends, or discover patterns of mutational hotspots when included in artificial neural
                            networks. Thus, a unique
                            method for understanding viral evolution from a changing, data-driven point of view is
                            provided by coupling
                            phylogenetic studies with neural signal processing.
                            
                            See Fig. 5, which shows the tree construction for different genomes with suitable
                            out-groups.
                        
 
                            Fig. 5: Phylogenetic relationships of Lassa virus genomes. Maximum-likelihood trees of the (a) S and (b) L segments demonstrate evolutionary diversification, with several lineages associated with neurological complications.
                            Evaluation of Phylogenetic Tree 
                            The tree reliability assessment process is a crucial step in determining the validity of the
                            selected data and
                            genomes, with 1000 replicates performed [26, 27]. See Fig. 6, which explains the evaluation
                            of the phylogenetic
                            tree (a) Segment S, and (b) Segment L.
                        
 
                            Fig. 6: Reliability of phylogenetic trees. Bootstrap analyses for the (a) S and (b) L segment trees demonstrate robustness of major clades, supporting evolutionary interpretations related to CNS involvement.
                            Finding SNPs and Mutations 
                            These SNPs, their locations, and types in the genome were identified, along with their
                            associated mutations.
                            This aim was achieved by using sequence alignment to compare all virus sequences and
                            identify the locations of
                            nucleotide variations in each of them [28, 29]. Comparison was used to analyze and precisely
                            identify the
                            locations of these mutations in each gene for each virus in the analysis. Refer to Tables 1
                            and 2 for the
                            classification and quantity of SNPs and mutations included in the genomes utilized in this
                            study.
                            
                            RAxML-NG, a more recent implementation of RAxML, was used to rebuild the ancestral genomes.
                            The procedure was
                            also enhanced with a maximum likelihood tree to perform ancestral reconstruction of genome
                            sequences by
                            comparing these genomes and identifying the most ancient common ancestor as the root of the
                            tree [30]. Refer to
                            Fig. 7, illustrating the restored ancestral tree where nodes 13 and 11 display the most
                            ancestral common
                            ancestor of the genomes used.
                            
                            Each node in the phylogenetic tree generated by the study may represent an alternative
                            ancestral genotype,
                            indicating the evolutionary connections among viral genomes. To be able to train intelligent
                            systems like
                            artificial neural networks (ANNs) on phylogenetic and genomic features to forecast future
                            evolutionary events,
                            track viral diversification, and discover early warning signs of possibly harmful mutations,
                            the tree structure
                            can be viewed as a neural network, with each internal node performing as a "processing
                            unit" of
                            biological data [31]. Reconstruction of the genome sequence of the earliest known common
                            ancestor of Lassa virus
                            (both S and L segments) is an important landmark in the understanding of the evolutionary
                            trends of the virus.
                            These reference sequences provide a stable basis against which to measure genetic variation
                            in modern strains
                            and compare emerging mutations across time and space. Based on the outcome of ancestral
                            reconstruction and
                            inference of SNPs, one can frame smart predictive models using artificial intelligence and
                            machine learning
                            algorithms. These models can infer evolutionary trends and predict the rise of new mutations
                            or the onset of
                            some strains spreading to new locations [32].
                            
                            The use of reconstructed ancient ancestral sequences is the foundation for building
                            intelligent surveillance
                            systems that can analyze current genetic isolates and compare them to the reference
                            ancestor, the root. This
                            enables us to detect recently occurring, potentially dangerous mutations and provide early
                            warning of
                            potentially dangerous mutations. Ultimately, this research is an effective contribution to
                            enabling future tools
                            to monitor Lassa virus with greater precision. It outlines an important framework for
                            improving epidemic
                            response and utilizing bioinformatics tools and intelligent systems to predict future viral
                            changes due to
                            various factors.
                            
                            The nucleotide sequences of the genome of the oldest common ancestor in this group and of
                            segments S and L,
                            represented by nodes 13 and 11, are shown in Supplemental Tables 1 and 2. This process is
                            crucial for
                            understanding the origin of the virus and how it has evolved. This process is achieved by
                            observing current
                            genomes and how they evolved from an older common ancestor and by comparing mutations and
                            changes that have
                            occurred over time to determine which parts have persisted from the ancient ancestor to the
                            current genomes and
                            which parts have been altered as a result of various factors, possibly climatic or
                            environmental.
                        
 
                            Table 1: Single nucleotide polymorphisms (SNPs) identified in Lassa virus genomes. The distribution of SNP types highlights substitution patterns that may influence viral replication and CNS involvement
 
                            Table 2: Mutational burden of Lassa virus segments. The L segment shows a particularly high mutation load, especially in the polymerase gene, which may affect replication fidelity and facilitate CNS persistence. Ancestral Sequence Reconstruction
 
                            Fig. 7: Ancestral genome reconstruction. Reconstructed sequences of (a) the S and (b) the L segments identify putative root genomes, providing references to trace mutations potentially linked to neurotropic adaptations.
                            The Impact of Lassa Virus Genetic Variability on Neurotropism and CNS Pathogenesis
                            
                            This study reveals novel genetic variants that may influence the functioning of the brain
                            and spinal cord
                            (central nervous system). Our findings offer substantial evidence for a correlation between
                            the identified
                            mutations and neurological manifestations, despite the processes involved remaining
                            inadequately elucidated. The
                            observation that numerous identified alterations occur in genes implicated in neuroimmune
                            regulation, synaptic
                            signaling, or neuronal development supports the hypothesis that these modifications may
                            enhance the
                            vulnerability of the central nervous system to injury [33].
                            
                            The neurological symptoms associated with mutations in related pathways align with the
                            clinical phenotypes
                            observed in affected patients, characterized by cognitive deficits and motor abnormalities
                            [34, 35]. While
                            experimental validation is limited, literature evidence points to similar mutations lead to
                            the disruption of
                            neuronal homeostasis, modified synaptic plasticity, or atypical neuroinflammatory responses.
                            These associations
                            underscore the need to consider CNS involvement when evaluating patients carrying these
                            variants [35, 36].
                            
                            These mutations can cause systemic problems that make CNS disease worse since central and
                            peripheral symptoms
                            can happen at the same time. According to this perspective, mutations exert effects that are
                            both
                            cell-autonomous within neurons and non-cell-autonomous via peripheral systems, aligning with
                            contemporary models
                            of neurogenetic disorders [37].
                            
                            Based on our findings, functional studies were recommended in neuronal models, and in
                                vivo systems must
                            be incorporated into future research to elucidate unique neuropathogenic pathways.
                            Establishing the significance
                            of these variations in central nervous system dysfunction and identifying potential
                            therapeutic targets
                            necessitates the integration of clinical, genetic, and mechanistic data. Overall, this study
                            contributes to a
                            growing recognition of the neurological dimension of these genetic alterations and provides
                            a foundation for
                            further neuroscience-focused investigations.
                            
                            This work presents a comprehensive bioinformatics analysis of the Arenaviridae family,
                            focusing specifically on
                            the neurotropic Lassa virus (LASV) strains. Our research has identified multiple anomalies
                            in the viral genome
                            that may account for the virus's ability to infect and persist in the central nervous
                            system, resulting in
                            diverse neurological manifestations.
                        
                            Genetic Variants and CNS Involvement 
                            The identified mutations predominantly affect regions of the LASV genome that are important
                            for viral
                            replication and host cell interaction. These alterations may enhance the virus's capacity to
                            cross the
                            blood-brain barrier (BBB) or evade immune surveillance within the CNS. Similar mechanisms
                            have been observed in
                            other neurotropic viruses, where specific genetic changes facilitate neuronal invasion and
                            persistence. For
                            instance, mutations in the LASV glycoprotein precursor (GPC) can influence the virus's
                            ability to infect
                            dendritic cells, which are essential for initiating immunological responses in the central
                            nervous system (CNS)
                            [38, 39]. Additionally, the neurotropic LCMV Clone 13 strain possesses mutations in the GPC
                            and polymerase genes
                            that enhance replication within dendritic cells. This, in turn, alters immunological
                            responses and may affect
                            the central nervous system (CNS) [40, 41].
                        
                            Neurological Manifestations in Lassa Fever 
                            Common neurological symptoms in people with LASV infection include loss of sensorineural
                            hearing, tremors,
                            encephalitis, and ataxia. These symptoms may manifest during the acute period or as
                            post-infectious
                            consequences. The etiology of these symptoms is influenced by various mechanisms, including
                            immune-mediated
                            damage, metabolic disturbances, and direct viral cytotoxicity. Recent studies indicate that
                            the Lassa fever
                            virus (LASSV) significantly contributes to sensorineural hearing loss, a common and often
                            irreversible outcome
                            of the disease. Scientists think that this condition happens when viruses harm the cochlear
                            structures or
                            auditory circuits [42]. The encephalitis and ataxia observed in individuals infected with
                            LASV may possibly
                            result from inflammatory responses and viral invasion of neuronal tissues
                        
                            Potential Neurological Implications of SNP Variability 
                            The functional implications of the identified single nucleotide polymorphisms (SNPs) in the
                            Lassa virus genome
                            may influence neurological outcomes, particularly given the frequency of T and A
                            substitutions. Previous
                            research [43, 44] indicates that viral mutations can influence neurotropism, the efficacy of
                            viral replication
                            in the CNS, and the host immune response. For example, single-nucleotide polymorphisms
                            (SNPs) that alter viral
                            proteins responsible for host-cell entry or immune evasion may induce CNS symptoms such as
                            encephalopathy or
                            neuroinflammation by modifying the virus's ability to cross the blood-brain barrier or
                            interact with neural
                            cells. Changes to viral gene expression caused by deletions or substitutions in regulatory
                            regions may also
                            affect neurovirulence. These results underscore the necessity of performing targeted
                            functional studies to
                            clarify the correlation between neurological sequelae in Lassa virus infection and specific
                            nucleotide
                            modifications.
                        
                            Implications of SNPs and Mutations on Neurological Outcomes 
                            Table 2 shows that the S and L segments of the Lassa virus have a lot of mutations and
                            single nucleotide
                            polymorphisms (SNPs). The L segment shows particularly extensive mutational burden. High
                            mutation rates,
                            especially in the L segment encoding the viral RNA-dependent RNA polymerase, may influence
                            viral replication
                            fidelity, host-cell interactions, and immune evasion—factors that are increasingly
                            recognized as relevant to CNS
                            involvement [43, 44]. Mutations in regulatory or structural genes may lead to an increase in
                            neurotropism or
                            alterations in the functions of viral proteins that interact with neural cells. For
                            instance, alterations to the
                            S segment, which codes for the nucleoprotein and glycoprotein precursor, may affect viral
                            entry into glial cells
                            or neurons and how the immune system reacts in the central nervous system. Such mutational
                            patterns may help
                            explain the neurological manifestations observed in some Lassa virus infections, including
                            encephalopathy and
                            cognitive deficits.
                        
                            Phylogenetic Analysis, SNPs, and Neurological Implications 
                            This study's phylogenetic analysis provides a detailed view of the evolutionary
                            relationships among Lassa virus
                            genomes. Importantly, these ancestral reconstructions, particularly of the earliest common
                            ancestor of both S
                            and L segments, offer a reference framework to compare contemporary mutations and track
                            viral evolution. High
                            mutational burdens observed in the L segment (Table 2), combined with the diverse SNP types
                            identified (Table
                            1), suggest ongoing viral adaptation that may influence host-pathogen interactions,
                            including in the central
                            nervous system (CNS). Several changes in structural and polymerase genes may influence viral
                            neurotropism, the
                            efficacy of viral replication in neural tissue, and immune responses inside the central
                            nervous system (CNS).
                            For instance, alterations in the S segment—which encodes glycoproteins involved in host cell
                            entry—could
                            theoretically facilitate viral penetration into neuronal or glial cells, contributing to the
                            neurological
                            manifestations documented in Lassa virus infection, such as encephalopathy and cognitive
                            impairments [45].
                            
                            The integration of phylogenetic insights with computational biology and statistical models
                            approaches holds
                            promise for predictive surveillance. Conceptualizing the phylogenetic tree as a network of
                            “processing units”
                            allows training of intelligent systems, such as artificial neural networks (ANNs), to
                            forecast future
                            evolutionary trends, identify emergent mutations, and potentially anticipate shifts that
                            could increase CNS
                            involvement. By comparing modern isolates to reconstructed ancestral sequences, AI-driven
                            models can detect
                            recently emerged, potentially neurovirulent mutations, offering an early-warning framework
                            for public health
                            interventions [31, 32]. Overall, the evolutionary history of the Lassa virus may be
                            elucidated, and potential
                            neurological risks can be anticipated, facilitated by the amalgamation of SNP analysis,
                            mutational mapping, and
                            ancestral genome reconstruction. This study integrates multiple disciplines, highlighting
                            the significance of
                            viral genomes in understanding the mechanisms by which viruses create neurological illnesses
                            and the potential
                            of contemporary bioinformatics techniques in mitigating or alleviating the severity of
                            virus-induced
                            neurological complications.
                            
                            Chika-Igwenyi et al. (2021) found that Lassa fever outbreaks in Ebonyi State, Nigeria,
                            exhibited notable
                            differences in epidemiology, clinical features, and outcomes. In the beginning of the
                            pandemic, neurological
                            symptoms were rare. However, as the outbreak went on, they grew more common and were linked
                            to a higher death
                            rate and worse cases. This was notably true during the second outbreak, when a greater case
                            fatality rate was
                            also seen. This meant that the involvement of lethal strain of the virus. Our study aligns
                            with these findings,
                            as the observed neurological manifestations may stem from a molecular basis in the virus's
                            evolution towards
                            heightened neurotropism and severity, elucidated by the identified SNPs and mutations in
                            both the S and L
                            segments, alongside the reconstructed ancestral sequences [35].
                            
                            McEntire et al. (2021) illustrated that a wide range of epidemic and pandemic diseases can
                            present with diverse
                            neurological manifestations, including central nervous system conditions such as meningitis,
                            encephalitis,
                            intraparenchymal hemorrhage, and seizures; peripheral and cranial nerve syndromes like
                            sensory neuropathy,
                            sensorineural hearing loss, and ophthalmoplegia; post-infectious syndromes including acute
                            inflammatory
                            polyneuropathy; and congenital syndromes such as fetal microcephaly. While some of these
                            diseases have
                            established therapies, others are managed primarily with supportive care. This perspective
                            complements our study
                            by highlighting the potential neurological implications of viral mutations, including those
                            identified in Lassa
                            virus, suggesting that specific SNPs or evolutionary changes may underline the neurological
                            outcomes observed in
                            severe cases [46].
                            
                            Okokhere et al. (2016) stated that Lassa virus can cause aseptic meningitis even if there is
                            no bleeding.
                            Patients who received ribavirin exhibited favorable outcomes and did not encounter any
                            prolonged neurological
                            complications. This aligns with our findings on the central nervous system's role in Lassa
                            virus infections,
                            underscoring the necessity for prompt diagnosis and customized antiviral treatment to
                            prevent neurological
                            sequelae [47].
                            
                            Saka et al. (2025) illustrated that people who survive Lassa fever often have hearing loss,
                            cognitive
                            impairment, seizures, delayed-onset paraparesis, and other neurological and sensory
                            problems, as well as eye and
                            mental problems. This study demonstrates that Lassa virus infection constitutes a
                            significant, enduring issue
                            for comprehensive treatment and rehabilitation. Our research also discovered that acute
                            infection might affect
                            the central nervous system and lead to neurological symptoms; this indicates that prompt
                            detection and treatment
                            may assist survivors in preventing long-term complications [48].
                            
                            Duvignaud et al. (2020) revealed a link between Lassa fever and delayed onset paraparesis,
                            indicating a
                            potential relationship between viral infection and spinal cord injury. Patients with Lassa
                            fever require
                            meticulous neurological surveillance, as this case illustrates the extensive array of
                            neurological complications
                            that may arise weeks subsequent to acute illness. Our study indicates that the central
                            nervous system is engaged
                            during acute infection, suggesting that both acute and delayed neurological symptoms must be
                            considered when
                            assessing the disease's severity and deciding patient treatment [49].
                            
                            Our study supports the notion that specific viral strains or mutations identified through
                            SNP and phylogenetic
                            analyses may contribute to the neurological manifestations of Lassa fever. This is
                            corroborated by Günther et
                            al. (2001), who detected viral RNA in cerebrospinal fluid but not in serum, suggesting that
                            the virus may
                            persist in the central nervous system and potentially influence neuropathogenesis [50].
                            
                            As of now, there are no vaccines or medicines that have been licensed to stop or treat Lassa
                            virus infection.
                            However, Raabe et al. (2022) demonstrated that many vaccine platforms are in pre-clinical
                            development and that
                            many antiviral candidates show promise as treatments or post-exposure prophylactics. The
                            review by Raabe et al.
                            (2022) emphasizes clinical strategies, including exploratory treatments and hospital
                            engineering controls, as
                            pragmatic approaches to managing suspected infections. This is relevant to our study, as
                            understanding the
                            expanding therapeutic landscape and potential therapies may influence strategies for
                            controlling the
                            neurological repercussions of Lassa fever, particularly in regions experiencing current
                            outbreaks and among
                            high-risk populations [51].
                            
                            Murphy and Ly (2021) emphasize that there are no vaccines or therapies that work completely
                            for the Lassa virus
                            (LASV) right now. About 37.7 million individuals in Africa are in danger of getting the
                            virus. In regions with
                            limited resources, accurate diagnosis of LASV might be difficult because the virus has a lot
                            of different
                            genetic variations and its symptoms are similar to those of other febrile infections.
                            Current diagnostics are
                            mostly laboratory-developed and not widely validated for clinical use, highlighting the
                            urgent need for simple,
                            affordable, and sensitive tests capable of distinguishing LASV lineages. Ribavirin and
                            supportive care are the
                            only drugs that have been approved for usage so far. However, ribavirin is contraindicated
                            during pregnancy, and
                            it only works in early administration. Several therapeutics and vaccines are in preclinical
                            development, though
                            very few have reached clinical testing. Continued research into LASV biology, immune
                            evasion, pathogenicity, and
                            vector ecology is crucial to guide the development of diagnostics, therapeutics, and
                            preventive strategies. In
                            light of this context, our study's focus on genetic variations and mutations is particularly
                            important, as it
                            may inform future surveillance, clinical management, and the formulation of effective
                            therapies [52].
                            
                            Electroencephalography (EEG) has demonstrated significant potential in identifying central
                            nervous system (CNS)
                            involvement in many viral infections, including Lassa fever, encephalitis, diminished
                            consciousness, and seizure
                            management. Mueller et al. (2024) showed that EEG may effectively identify neurological
                            issues in high-risk,
                            resource-constrained environments, despite challenges such as technical artifacts,
                            environmental influences, and
                            biosafety limitations, when conducted by proficient neurophysiologists. These results
                            corroborate our study's
                            findings on central nervous system involvement linked to viral genetic variants [53],
                            underscoring the necessity
                            of using neurodiagnostic tools in the assessment of patients with Lassa virus infection.
                        
                            Implications for Future Research 
                            Experimental validation is essential to confirm the associations identified by our
                            bioinformatics method about
                            neurotropism. To elucidate how these modifications facilitate CNS invasion and persistence,
                            it is imperative to
                            do functional studies utilizing animal models, neuronal cell cultures, and advanced imaging
                            technologies.
                            
                            Moreover, understanding the host factors that interact with these viral mutations could
                            provide insights into
                            the variability of neurological outcomes observed in LASV infections. Identifying genetic
                            predispositions or
                            immune responses that influence CNS involvement may lead to personalized therapeutic
                            strategies aimed at
                            mitigating neurological complications.
                            
                            In general, the results suggest that Lassa virus (LASV) can infect the central nervous
                            system (CNS) because of
                            several mutations and genetic variations observed in the S and L segments. Our research
                            suggests that these
                            viral changes could affect neuroinvasion, persistence, and how the virus interacts with the
                            host's immune
                            system. This may elucidate the occurrence of symptoms such as encephalopathy, cognitive
                            deficits, and
                            sensorineural hearing loss after acute infections. In line with clinical observations from
                            earlier outbreaks and
                            case reports, the combination of SNP analysis, mutational mapping, and phylogenetic
                            reconstruction creates a
                            framework for following the evolution of viruses and predicting neurovirulent strains. These
                            results underscore
                            the importance of early diagnosis of CNS involvement and targeted treatment strategies,
                            while more experimental
                            validation is necessary. In summary, the findings of this study establish a foundation for
                            further research into
                            the neuropathogenesis of Lassa fever virus (LASSV) that combines bioinformatics, clinical,
                            and mechanistic
                            approaches; such research should facilitate the development of diagnostic tools, therapies,
                            and preventive
                            strategies for neurological complications associated with Lassa fever.
                        
By choosing the right genomes and out-group elements for both groups, we were able to create accurate and connected trees to trace the evolutionary history of the Arenavirus family. By identifying SNPs and mutations that occurred in the origin of the virus, that is, from the ancient ancestor to the current strains—we can link these mutations to specific traits, such as drug resistance, increased disease severity, or changes in transmission. By sequencing the ancient common ancestor and identifying target regions shared by all genome lineages, we can develop treatments and vaccines. These sites could be suitable targets for vaccines or therapeutics against most genomes. We succeeded in determining how this important group of viruses is related using careful alignment, selection of appropriate models, efficient tree construction methods, and statistical analysis. Reconstructing the original genome sequences and discovering the mutations that cause these changes helps us understand how the virus has adapted and evolved, opening up new research avenues to combat the diseases it causes.
The authors have nothing to disclose.
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