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The 2026 Bundibugyo Ebola Outbreak: A Warning for Global Preparedness for Future Epidemics.

Dear Editor, The 2026 Bundibugyo Ebolavirus (BDBV) outbreak has once again demonstrated that the threat of emerging diseases remains a major global health challenge. The outbreak, first detected in the Democratic Republic of Congo (DRC) and spread to Uganda, is not only a regional crisis but also a test of the world's preparedness for pathogens with epidemic potential. Unlike Zaire Ebolavirus (EBOV), which has benefited from effective vaccines and treatments in recent years, BDBV still lacks a licensed vaccine or specific treatment[1]. As of June 6, a total of 515 laboratory-confirmed cases and 91 deaths have been reported in DRC, while Uganda has reported 19 laboratory-confirmed cases and two deaths. The occurrence of unexplained deaths among both the community and healthcare workers, along with prior reports of an unidentified hemorrhagic fever, suggest that the outbreak has been likely originated in March 2026 or even earlier. Accordingly, the virus is believed to have spread unnoticed for several weeks before being identified through genomic sequencing in mid-May 2026[2]. The resurgence of Ebola in Africa results from a complex interaction of environmental, social, and political factors. Deforestation, the development of mining activities, the expansion of agriculture, and increased human contact with wildlife have elevated the likelihood of spillovers from wildlife reservoirs, particularly fruit bats, which are considered the most likely natural hosts of ebolaviruses. Moreover, weak disease surveillance systems and limited access to health services have delayed the identification of early cases. The similarity of the initial symptoms of Ebola to other endemic diseases in the region, such as malaria, makes early diagnosis difficult and provides ample opportunity for transmission to spread. Insecurity, misinformation, attacks on healthcare facilities, and armed conflict in the region have also posed serious challenges to the implementation of contact tracing programs and rapid response to the epidemic[3,4]. One of the most critical challenges highlighted by this outbreak is the weakness of diagnostic capacities in the affected areas. The initial 2007 outbreak of BDBV proved that delayed lab confirmation paralyzes public health responses[5]. Now, dealing with a much larger outbreak in 2026, the persistence of this challenge highlights a dangerous failure to invest in diagnostic infrastructure over the last 19 years. Many health facilities do not have access to molecular laboratories, rapid sample transport systems, and biosafety infrastructure[6]. These limitations delay the diagnosis and isolation of patients, thus perpetuating disease transmission. Investment in the development of mobile laboratories, rapid point-of-care diagnostic tests, and digital reporting systems can dramatically reduce the time to diagnosis and response to an outbreak. The BDBV outbreak shows that laboratory preparedness must be considered an essential part of global health security. Furthermore, the early detection of emerging pathogens depends not only on diagnostic technologies but also on the expertise of local scientists who are able to recognize unusual epidemiological and laboratory patterns. During the current outbreak, suspected Ebola cases initially tested negative using common diagnostic tests (designed for Zaire Ebola Virus), which delayed the identification of the BDBV. Specifically, field-based diagnostics in Bunia were calibrated exclusively to detect the EBOV responsible for recent Congolese outbreaks. Consequently, patient samples collected throughout late April and early May yielded negative results, requiring cross-country transport to Kinshasa for genomic confirmation[2]. This experience revealed a major vulnerability in outbreak preparedness: diagnostic tools designed for known threats may be ineffective in detecting less common or unexpected pathogens. Therefore, strengthening local scientific capacities, developing genomic surveillance, and expanding access to flexible and adaptable diagnostic platforms should be considered as a top priority for global health security. The lack of a licensed vaccine for BDBV was one of the most significant challenges of this epidemic. While the rVSV-ZEBOV vaccine has played a significant role in controlling Zaire ebolavirus, there is no licensed vaccine for BDBV. In response to this outbreak, efforts to develop mRNA-based vaccines, adenoviral vectors, rVSV-based vaccines, and multipotent vaccines have been accelerated[7]. However, the experience of this epidemic has shown that the development of medical products for rare diseases continues to face financial and investment constraints. This challenge highlights the need for sustained support from governments and international institutions for research and development of pathogens with epidemic potential. The 2026 Bundibugyo outbreak provides several key lessons for the global community. First, early detection and rapid diagnosis are the most important factors in containing the epidemic. The 19-year interval between the 2007 BDBV outbreak and the 2026 outbreak underscores persistent shortcomings in investment toward decentralized, pan-ebolavirus diagnostic infrastructure, with diagnostic delays hindering timely outbreak identification in both instances. Second, the trust and active participation of local communities are as important as medical interventions. Additionally, the rapid cross-border transmission dynamics between the DRC and Uganda demonstrate that blanket travel restrictions and border closures are impractical. As communities in the Great Lakes region routinely cross national borders for trade and healthcare, coordinated regional surveillance and timely information sharing are likely to be more effective than broad border closures in mitigating disease transmission[8]. Third, the protection of health workers must be a priority in preparedness plans. Fourth, a "One Health" approach is essential for simultaneous monitoring of humans, animals, and the environment. Although BDBV is not a new pathogen, the lack of licensed medical interventions and limited investment in research reflect many of the vulnerabilities associated with the concept of "Disease X."[9]. Unlike Zaire Ebola Virus, for which licensed vaccines and monoclonal antibody therapies are available, BDBV forces public health responses to rely almost entirely on non-pharmaceutical interventions such as isolation and infection control[10]. This gap reflects the structural inequity in global health research and development funding, with pathogens affecting resource-limited regions receiving insufficient attention until they spark an international emergency[2]. The BDBV outbreak proves that global epidemic preparedness cannot be pathogen-selective; it requires proactive investment in broad-spectrum countermeasures and resilient frontline health systems[8]. In conclusion, the 2026 BDBV outbreak is a serious wake-up call for the global health system. The epidemic revealed that gaps in surveillance systems, diagnostic capacities, vaccine development, and preparedness for emerging diseases persist. Investing in health infrastructure, developing Pan-Ebolavirus vaccines, strengthening laboratories, expanding the One-Health approach, and supporting research on emerging zoonotic pathogens must be at the top of global health security priorities. Otherwise, the BDBV outbreak may be just a prelude to larger crises to come.

Ebolavirus↗

Neural system-enriched gene expression: relationship to biological pathways and neurological diseases.

To understand the commitment of the genome to nervous system differentiation and function, we sought to compare nervous system gene expression to that of a wide variety of other tissues by gene expression database construction and mining. Gene expression profiles of 10 different adult nervous tissues were compared with that of 72 other tissues. Using ANOVA, we identified 1,361 genes whose expression was higher in the nervous system than other organs and, separately, 600 genes whose expression was at least threefold higher in one or more regions of the nervous system compared with their median expression across all organs. Of the 600 genes, 381 overlapped with the 1,361-gene list. Limited in situ gene expression analysis confirmed that identified genes did represent nervous system-enriched gene expression, and we therefore sought to evaluate the validity and significance of these top-ranked nervous system genes using known gene literature and gene ontology categorization criteria. Diverse functional categories were present in the 381 genes, including genes involved in intracellular signaling, cytoskeleton structure and function, enzymes, RNA metabolism and transcription, membrane proteins, as well as cell differentiation, death, proliferation, and division. We searched existing public sites and identified 110 known genes related to mental retardation, neurological disease, and neurodegeneration. Twenty-one of the 381 genes were within the 110-gene list, compared with a random expectation of 5. This suggests that the 381 genes provide a candidate set for further analyses in neurological and psychiatric disease studies and that as a field, we are as yet, far from a large-scale understanding of the genes that are critical for nervous system structure and function. Together, our data indicate the power of profiling an individual biologic system in a multisystem context to gain insight into the genomic basis of its structure and function.

Animals↗

Consolidating the set of known human protein-protein interactions in preparation for large-scale mapping of the human interactome.

BACKGROUND: Extensive protein interaction maps are being constructed for yeast, worm, and fly to ask how the proteins organize into pathways and systems, but no such genome-wide interaction map yet exists for the set of human proteins. To prepare for studies in humans, we wished to establish tests for the accuracy of future interaction assays and to consolidate the known interactions among human proteins. RESULTS: We established two tests of the accuracy of human protein interaction datasets and measured the relative accuracy of the available data. We then developed and applied natural language processing and literature-mining algorithms to recover from Medline abstracts 6,580 interactions among 3,737 human proteins. A three-part algorithm was used: first, human protein names were identified in Medline abstracts using a discriminator based on conditional random fields, then interactions were identified by the co-occurrence of protein names across the set of Medline abstracts, filtering the interactions with a Bayesian classifier to enrich for legitimate physical interactions. These mined interactions were combined with existing interaction data to obtain a network of 31,609 interactions among 7,748 human proteins, accurate to the same degree as the existing datasets. CONCLUSION: These interactions and the accuracy benchmarks will aid interpretation of current functional genomics data and provide a basis for determining the quality of future large-scale human protein interaction assays. Projecting from the approximately 15 interactions per protein in the best-sampled interaction set to the estimated 25,000 human genes implies more than 375,000 interactions in the complete human protein interaction network. This set therefore represents no more than 10% of the complete network.

Algorithms↗

Computing motif correlations in proteins.

Protein motifs, which are specific regions and conserved regions, are found by comparing multiple protein sequences. These conserved regions in general play an important role in protein functions and protein folds, for example, for their binding properties or enzymatic activities. The aim here is to find the existence correlations of protein motifs. The knowledge of protein motif/domain sharing should be important in shedding new light on the biologic functions of proteins and offering a basis in analyzing the evolution in the human genome or other genomes. The protein sequences used here are obtained from the PIR-NREF database and the protein motifs are retrieved from the PROSITE database. We apply data mining approach to discover the occurrence correlations of motif in protein sequences. The correlation of motifs mined can be used in evolution analyses and protein structure prediction. We discuss the latter, i.e., protein structure prediction in this study. The correlations mined are stored and maintained in a database system. The database is now available at http://bioinfo.csie.ncu.edu.tw/ProMotif/.

Algorithms↗

Neuropeptides and their precursors in the fruitfly, Drosophila melanogaster.

Neuropeptides form the most diverse class of chemical messenger molecules in metazoan nervous systems. They are usually generated from biosynthetic precursor polypeptides by enzymatic processing and modification. Many different peptides belonging to a number of distinct neuropeptide families have already been characterized from various insect species. The Drosophila Genome Sequencing Project has important implications for the future of neurobiological research. This paper describes the discovery of several new fruitfly neuropeptides by an in silico data mining approach. In addition, the state-of-the-art of Drosophila peptide research is reviewed.

Amino Acid Sequence↗

arrayCGHbase: an analysis platform for comparative genomic hybridization microarrays.

BACKGROUND: The availability of the human genome sequence as well as the large number of physically accessible oligonucleotides, cDNA, and BAC clones across the entire genome has triggered and accelerated the use of several platforms for analysis of DNA copy number changes, amongst others microarray comparative genomic hybridization (arrayCGH). One of the challenges inherent to this new technology is the management and analysis of large numbers of data points generated in each individual experiment. RESULTS: We have developed arrayCGHbase, a comprehensive analysis platform for arrayCGH experiments consisting of a MIAME (Minimal Information About a Microarray Experiment) supportive database using MySQL underlying a data mining web tool, to store, analyze, interpret, compare, and visualize arrayCGH results in a uniform and user-friendly format. Following its flexible design, arrayCGHbase is compatible with all existing and forthcoming arrayCGH platforms. Data can be exported in a multitude of formats, including BED files to map copy number information on the genome using the Ensembl or UCSC genome browser. CONCLUSION: ArrayCGHbase is a web based and platform independent arrayCGH data analysis tool, that allows users to access the analysis suite through the internet or a local intranet after installation on a private server. ArrayCGHbase is available at http://medgen.ugent.be/arrayCGHbase/.

Base Sequence↗

Genomics, evolution and biological functions of the pacifastin peptide family: a conserved serine protease inhibitor family in arthropods.

The last decade, a new serine protease inhibitor family has been described in arthropods. Eight members were purified from the locusts Locusta migratoria (LMPI-1-2 and HI) and Schistocerca gregaria (SGPI-1-5). The light chain of the heterodimeric protease inhibitor pacifastin, from the freshwater crayfish Pacifastacus leniusculus, was found to be composed of nine consecutive inhibitory domains (PLDs). These domains share a pattern of six conserved cysteine residues (Cys-Xaa(9-12)-Cys-Asn-Xaa-Cys-Xaa-Cys-Xaa(2-3)-Gly-Xaa(3-6)-Cys-Thr-Xaa(3)-Cys) with the locust inhibitors. Via cDNA cloning, eight pacifastin-related precursors have been identified in locusts. Interestingly, additional pacifastin-related precursors have been identified in Diptera, Lepidoptera and Coleoptera utilising an in silico data mining approach.

Amino Acid Sequence↗

GenomeTrafac: a whole genome resource for the detection of transcription factor binding site clusters associated with conventional and microRNA encoding genes conserved between mouse and human gene orthologs.

Transcriptional cis-regulatory control regions frequently are found within non-coding DNA segments conserved across multi-species gene orthologs. Adopting a systematic gene-centric pipeline approach, we report here the development of a web-accessible database resource--GenomeTraFac (http://genometrafac.cchmc.org)--that allows genome-wide detection and characterization of compositionally similar cis-clusters that occur in gene orthologs between any two genomes for both microRNA genes as well as conventional RNA-encoding genes. Each ortholog gene pair can be scanned to visualize overall conserved sequence regions, and within these, the relative density of conserved cis-element motif clusters form graph peak structures. The results of these analyses can be mined en masse to identify most frequently represented cis-motifs in a list of genes. The system also provides a method for rapid evaluation and visualization of gene model-consistency between orthologs, and facilitates consideration of the potential impact of sequence variation in conserved non-coding regions to impact complex cis-element structures. Using the mouse and human genomes via the NCBI Reference Sequence database and the Sanger Institute miRBase, the system demonstrated the ability to identify validated transcription factor targets within promoter and distal genomic regulatory regions of both conventional and microRNA genes.

Animals↗

SPINE: an integrated tracking database and data mining approach for identifying feasible targets in high-throughput structural proteomics.

High-throughput structural proteomics is expected to generate considerable amounts of data on the progress of structure determination for many proteins. For each protein this includes information about cloning, expression, purification, biophysical characterization and structure determination via NMR spectroscopy or X-ray crystallography. It will be essential to develop specifications and ontologies for standardizing this information to make it amenable to retrospective analysis. To this end we created the SPINE database and analysis system for the Northeast Structural Genomics Consortium. SPINE, which is available at bioinfo.mbb.yale.edu/nesg or nesg.org, is specifically designed to enable distributed scientific collaboration via the Internet. It was designed not just as an information repository but as an active vehicle to standardize proteomics data in a form that would enable systematic data mining. The system features an intuitive user interface for interactive retrieval and modification of expression construct data, query forms designed to track global project progress and external links to many other resources. Currently the database contains experimental data on 985 constructs, of which 740 are drawn from Methanobacterium thermoautotrophicum, 123 from Saccharomyces cerevisiae, 93 from Caenorhabditis elegans and the remainder from other organisms. We developed a comprehensive set of data mining features for each protein, including several related to experimental progress (e.g. expression level, solubility and crystallization) and 42 based on the underlying protein sequence (e.g. amino acid composition, secondary structure and occurrence of low complexity regions). We demonstrate in detail the application of a particular machine learning approach, decision trees, to the tasks of predicting a protein's solubility and propensity to crystallize based on sequence features. We are able to extract a number of key rules from our trees, in particular that soluble proteins tend to have significantly more acidic residues and fewer hydrophobic stretches than insoluble ones. One of the characteristics of proteomics data sets, currently and in the foreseeable future, is their intermediate size ( approximately 500-5000 data points). This creates a number of issues in relation to error estimation. Initially we estimate the overall error in our trees based on standard cross-validation. However, this leaves out a significant fraction of the data in model construction and does not give error estimates on individual rules. Therefore, we present alternative methods to estimate the error in particular rules.

Animals↗

Protein flexibility and drug design: how to hit a moving target.

The most advanced methods for computer-aided drug design and database mining incorporate protein flexibility. Such techniques are not only needed to obtain proper results; they are also critical for dealing with the growing body of information from structural genomics.

Animals↗

HCAD, closing the gap between breakpoints and genes.

Recurrent chromosome aberrations are an important resource when associating human pathologies to specific genes. However, for technical reasons a large number of chromosome breakpoints are defined only at the level of cytobands and many of the genes involved remain unidentified. We developed a web-based information system that mines the scientific literature and generates textual and comprehensive information on all human breakpoints. We show that the statistical analysis of this textual information and its combination with genomic data can identify genes directly involved in DNA rearrangements. The Human Chromosome Aberration Database (HCAD) is publicly accessible at http://www.pdg.cnb.uam.es/UniPub/HCAD/.

Chromosome Breakage↗

Introns regulate RNA and protein abundance in yeast.

The purpose of introns in the architecturally simple genome of Saccharomyces cerevisiae is not well understood. To assay the functional relevance of introns, a series of computational analyses and several detailed deletion studies were completed on the intronic genes of S. cerevisiae. Mining existing data from genomewide studies on yeast revealed that intron-containing genes produce more RNA and more protein and are more likely to be haplo-insufficient than nonintronic genes. These observations for all intronic genes held true for distinct subsets of genes including ribosomal, nonribosomal, duplicated, and nonduplicated. Corroborating the result of computational analyses, deletion of introns from three essential genes decreased cellular RNA levels and caused measurable growth defects. These data provide evidence that introns improve transcriptional and translational yield and are required for competitive growth of yeast.

Actins↗

Exploring alternative transcript structure in the human genome using blocks and InterPro.

Understanding how alternative splicing affects gene function is an important challenge facing modern-day molecular biology. Using homology-based, protein sequence analysis methods, it should be possible to investigate how transcript diversity impacts protein function. To test this, high-quality exon-intron structures were deduced for over 8000 human genes, including over 1300 (17 percent) that produce multiple transcript variants. A data mining technique (DiffMotif) was developed to identify genes in which transcript variation coincides with changes in conserved motifs between variants. Applying this method, we found that 30 percent of the multi-variant genes in our test set exhibited a differential profile of conserved InterPro and/or BLOCKS motifs across different mRNA variants. To investigate these, a visualization tool (ProtAnnot) that displays amino acid motifs in the context of genomic sequence was developed. Using this tool, genes revealed by the DiffMotif method were analyzed, and when possible, hypotheses regarding the potential role of alternative transcript structure in modulating gene function were developed. Examples of these, including: MEOX1, a homeobox-containing protein; AIRE, involved in auto-immune disease; PLAT, tissue type plasminogen activator; and CD79b, a component of the B-cell receptor complex, are presented. These results demonstrate that amino acid motif databases like BLOCKS and InterPro are useful tools for investigating how alternative transcript structure affects gene function.

Algorithms↗

cis-regulatory region analysis using BEARR.

Genome-wide studies are fast becoming the norm, partly fueled by the availability of genome sequences and the feasibility of high-throughput experimental platforms, e.g., microarrays. An important aspect in any genome-wide studies is determination of regulatory relationships, believed to be primarily transacted through transcription factor binding to DNA. Identification of specific transcription factor binding sites in the cis-regulatory regions of genes makes it possible to list direct targets of transcription factors, model transcriptional regulatory networks, and mine other associated datasets for relevant targets for experimental and clinical manipulation. We have developed a web-based tool to assist biologists in efficiently carrying out the analysis of genes from studies of specific transcription factors or otherwise. The batch extraction and analysis of cis-regulatory regions (BEARR) facilitates identification, extraction, and analysis of regulatory regions from the large amount of data that is typically generated in genome-wide studies. This chapter highlights features and serves as a tutorial for using this publicly available software. The URL is http://giscompute.gis.a-star.edu.sg/~vega/BEARR1.0/.

Binding Sites↗

Design and characterization of a functional library for NMR screening against novel protein targets.

In the past few years, NMR has been extensively utilized as a screening tool for drug discovery using various types of compound libraries. The designs of NMR specific chemical libraries that utilize a fragment-based approach based on drug-like characteristics have been previously reported. In this article, a new type of compound library will be described that focuses on aiding in the functional annotation of novel proteins that have been identified from various ongoing genomics efforts. The NMR functional chemical library is comprised of small molecules with known biological activity such as: co-factors, inhibitors, metabolites and substrates. This functional library was developed through an extensive manual effort of mining several databases based on known ligand interactions with protein systems. In order to increase the efficiency of screening the NMR functional library, the compounds are screened as mixtures of 3-4 compounds that avoids the need to deconvolute positive hits by maintaining a unique NMR resonance and function for each compound in the mixture. The functional library has been used in the identification of general biological function of hypothetical proteins identified from the Protein Structure Initiative.

Binding Sites↗

Complete nucleotide sequence of the chloroplast genome from the green alga Chlorella vulgaris: the existence of genes possibly involved in chloroplast division.

The complete nucleotide sequence of the chloroplast genome (150,613 bp) from the unicellular green alga Chlorella vulgaris C-27 has been determined. The genome contains no large inverted repeat and has one copy of rRNA gene cluster consisting of 16S, 23S, and 5S rRNA genes. It contains 31 tRNA genes, of which the tRNALeu(GAG) gene has not been found in land plant chloroplast DNAs analyzed so far. Sixty-nine protein genes and eight ORFs conserved with those found in land plant chloroplasts have also been found. The most striking is the existence of two adjacent genes homologous to bacterial genes involved in cell division, minD and minE, which are arranged in the same order in Escherichia coli. This finding suggests that the mechanism of chloroplast division is similar to bacterial division. Other than minD and minE homologues, genes encoding ribosomal proteins L5, L12, L19, and S9 (rpl5, rpl12, rpl19, and rps9); a chlorophyll biosynthesis Mg chelating subunit (chlI); and elongation factor EF-Tu (tufA), which have not been reported from land plant chloroplast DNAs, are present in this genome. However, many of the new chloroplast genes recently found in red and brown algae have not been found in C. vulgaris. Furthermore, this algal species possesses two long ORFs related to ycf1 and ycf2 that are exclusively found in land plants. These observations suggest that C. vulgaris is closer to land plants than to red and brown algae.

Base Sequence↗