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Human papillomavirus 16 (HPV16) drives precursor cervical lesions that often progress to cervical cancer (CC). Variation within the HPV16 genome has been associated with CC risk. Here, we developed an affordable and portable amplicon-based long-read whole genome sequencing (WGS) approach using Oxford Nanopore Technologies to investigate HPV16 genetic diversity among women in sub-Saharan African countries. Applied to a control CaSki cell line and clinical samples (n = 12), our method generated complete HPV16 genomes at high coverage (median read coverage: 5,899–15,279 ×). Benchmarking our HPV16 controls showed high accuracy for two variant calling pipelines (Clair3 and PEPPER-Margin DeepVariant). Phylogenetic analysis identified all four previously defined HPV16 lineages (A–D) and their high-risk sublineages. All lineages exhibited strong concordance across de novo assembly, reference-based phylogenetics, and unsupervised clustering. Our pipeline effectively captured the full extent of genomic variation, including putative lineage-informative SNPs. This method offers a robust amplicon-based WGS and analysis pipeline for HPV16, making it well-suited for integration into surveillance, diagnostics, and epidemiological efforts in low-resource areas.

African swine fever virus (ASFV) is a highly contagious and fatal hemorrhagic disease of domestic pigs, which poses a major threat to the swine industry worldwide. Studies have shown that indigenous African pigs tolerate ASFV infection better than European pigs. The porcine v-rel avian reticuloendotheliosis viral oncogene homolog A (RelA) encoding a p65 kD protein, a major subunit of the NF-kB transcription factor, plays important roles in controlling both innate and adaptive immunity during infection with ASFV. In the present study, RelA genes from ASFV-surviving and symptomatic pigs were sequenced and found to contain polymorphisms revealing two discrete RelA amino acid sequences. One was found in the surviving pigs, and the other in symptomatic pigs. In total, 16 nonsynonymous SNPs (nsSNPs) resulting in codon changes were identified using bioinformatics software (SIFT and Polyphen v2) and web-based tools (MutPre and PredictSNP). Seven nsSNPs (P374-S, T448-S, P462-R, V464-P, Q478-H, L495-E, and P499-Q) were predicted to alter RelA protein function and stability, while 5 of these (P374-S, T448-S, P462-R, L495-E, and Q499-P) were predicted as disease-related SNPs. Additionally, the inflammatory cytokine levels of IFN-α, IL-10, and TNF-α at both the protein and the mRNA transcript levels were measured using ELISA and Real-Time PCR, respectively. The resulting data was used in correlation analysis to assess the association between cytokine levels and the RelA gene expression. Higher levels of IFN-α and detectable levels of IL-10 protein and RelA mRNA were observed in surviving pigs compared to healthy (non-infected). A positive correlation of IFN-α cytokine levels with RelA mRNA expression was also obtained. In conclusion, 7 polymorphic events in the coding region of the RelA gene may contribute to the tolerance of ASFV in pigs.

Tuberculosis (TB) poses a significant global health threat, with high mortality rates if left untreated. Current sputum-based TB treatment monitoring methods face numerous challenges, particularly in relation to sample collection and analysis. This pilot study explores the potential of TB status assessment using DNA methylation (DNAm) signatures, which are gaining recognition as diagnostic and predictive tools for various diseases. We collected buccal swab samples from pulmonary TB patients at the commencement of TB treatment (n = 10), and at one, two, and six-month follow-up intervals. We also collected samples from healthy controls (n = 10) and individuals exposed to TB (n = 10). DNAm patterns were mapped using the Illumina Infinium Methylation EPIC 850 K platform. A DNAm profile distinct from controls was discovered in the oral mucosa of TB patients at the start of treatment, and this profile changed throughout the course of TB treatment. These findings were corroborated in a separate validation cohort of TB patients (n = 41), monitored at two and six months into their TB treatment. We developed a machine learning model to predict symptom scores using the identified DNAm TB profile. The model was trained and evaluated on the pilot, validation, and two additional independent cohorts, achieving an R 2 of 0.80, Pearson correlation of 0.90, and mean absolute error of 0.13. While validation is needed in larger cohorts, the result opens the possibility of employing DNAm-based diagnostic and prognostic tools for TB in future clinical practice.

We evaluated therapeutic TAT for a tertiary hospital in Western Kenya, using a time-motion study focusing specifically on common hematology and biochemistry orders. The aim was to determine significant bottlenecks in diagnostic testing processes at the institution. A total of 356 (155 hematology and 201 biochemistry) laboratory tests were fully tracked from the time of ordering to availability of results to care providers. The total therapeutic TAT for all tests was 21.5 ± 0.249 hours (95% CI). The therapeutic TAT for hematology was 20.3 ± 0.331 hours (95% CI) while that for biochemistry tests was 22.2 ± 0.346 hours (95% CI). Printing, sorting and dispatch of the printed results emerged as the most significant bottlenecks, accounting for up to 8 hours of delay (Hematology-8.3 ± 1.29 hours (95% CI), Biochemistry-8.5 ± 1.18 hours (95% CI)). Time of test orders affected TAT, with orders made early in the morning and those in the afternoon experiencing the most delays in TAT. Significant inefficiencies exist at multiple steps in the turnaround times for routine laboratory tests at a large referral hospital within an LMIC setting. Multiple opportunities exist to improve TAT and streamline processes around diagnostic testing in this and other similar settings.

The coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with clinical manifestation cases that are almost similar to those of common respiratory viral infections. This study determined the prevalence of SARS-CoV-2 and other acute respiratory viruses among patients with flu-like symptoms in Bukavu city, Democratic Republic of Congo. We screened 1352 individuals with flu-like illnesses seeking treatment in 10 health facilities. Nasopharyngeal swab specimens were collected to detect SARS-CoV-2 using real-time reverse transcription-polymerase chain reaction (RT-PCR), and 10 common respiratory viruses were detected by multiplex reverse transcription-polymerase chain reaction assay. Overall, 13.9% (188/1352) of patients were confirmed positive for SARS-CoV-2. Influenza A 5.6% (56/1352) and Influenza B 0.9% (12/1352) were the most common respiratory viruses detected. Overall, more than two cases of the other acute respiratory viruses were detected. Frequently observed symptoms associated with SARS-CoV-2 positivity were shivering (47.8%; OR = 1.8; CI: 0.88–1.35), cough (89.6%; OR = 6.5, CI: 2.16–28.2), and myalgia and dizziness (59.7%; OR = 2.7; CI: 1.36–5.85). Moreover, coinfection was observed in 12 (11.5%) specimens. SARS-CoV-2 and influenza A were the most cooccurring infections, accounting for 33.3% of all positive cases. This study demonstrates cases of COVID-19 infections cooccurring with other acute respiratory infections in Bukavu city during the ongoing outbreak of COVID-19. Therefore, testing for respiratory viruses should be performed in all patients with flu-like symptoms for effective surveillance of the transmission patterns in the COVID-19 affected areas for optimal treatment and effective disease management.

HSP90 is a ubiquitously expressed chaperone protein that regulates the maturation of numerous substrate proteins called ‘clients’. The glycoprotein fibronectin (FN) is an important protein of the extracellular matrix (ECM) and a client protein of HSP90. FN and HSP90 interact directly, and the FN ECM is regulated by exogenous HSP90 or HSP90 inhibitors. Here, we extend the analysis of the HSP90 – FN interaction. The importance of the N-terminal 70-kDa fragment of fibronectin (FN70) and FN type I repeat was demonstrated by competition for FN binding between HSP90 and the functional upstream domain (FUD) of the Streptococcus pyogenes F1 adhesin protein. Furthermore, His-HSP90α mutations F352A and Y528A (alone and in combination) reduced the association with full-length FN (FN-FL) and FN70 in vitro. Unlike wild type His-HSP90α, these HSP90 mutants did not enhance FN matrix assembly in the Hs578T cell line model when added exogenously. Interestingly, the HSP90 E353A mutation, which did not significantly reduce the HSP90 – FN interaction in vitro, dramatically blocked FN matrix assembly in Hs578T cell-derived matrices. Taken together, these data extend our understanding of the role of HSP90 in FN fibrillogenesis and suggest that promotion of FN ECM assembly by HSP90 is not solely regulated by the affinity of the direct interaction between HSP90 and FN.

HSP90 is a ubiquitously expressed chaperone protein that regulates the maturation of numerous substrate proteins called ‘clients’. The glycoprotein fibronectin (FN) is an important protein of the extracellular matrix (ECM) and a client protein of HSP90. FN and HSP90 interact directly, and the FN ECM is regulated by exogenous HSP90 or HSP90 inhibitors. Here, we extend the analysis of the HSP90 – FN interaction. The importance of the N-terminal 70-kDa fragment of fibronectin (FN70) and FN type I repeat was demonstrated by competition for FN binding between HSP90 and the functional upstream domain (FUD) of the Streptococcus pyogenes F1 adhesin protein. Furthermore, His-HSP90α mutations F352A and Y528A (alone and in combination) reduced the association with full-length FN (FN-FL) and FN70 in vitro . Unlike wild type His-HSP90α, these HSP90 mutants did not enhance FN matrix assembly in the Hs578T cell line model when added exogenously. Interestingly, the HSP90 E353A mutation, which did not significantly reduce the HSP90 – FN interaction in vitro , dramatically blocked FN matrix assembly in Hs578T cell-derived matrices. Taken together, these data extend our understanding of the role of HSP90 in FN fibrillogenesis and suggest that promotion of FN ECM assembly by HSP90 is not solely regulated by the affinity of the direct interaction between HSP90 and FN.

HSP90 is a ubiquitously expressed chaperone protein that regulates the maturation of numerous substrate proteins called 'clients'. The glycoprotein fibronectin (FN) is an important protein of the extracellular matrix (ECM) and a client protein of HSP90. FN and HSP90 interact directly, and the FN ECM is regulated by exogenous HSP90 or HSP90 inhibitors. Here, we extend the analysis of the HSP90 - FN interaction. The importance of the N-terminal 70-kDa fragment of fibronectin (FN70) and FN type I repeat was demonstrated by competition for FN binding between HSP90 and the functional upstream domain (FUD) of the Streptococcus pyogenes F1 adhesin protein. Furthermore, His-HSP90α mutations F352A and Y528A (alone and in combination) reduced the association with full-length FN (FN-FL) and FN70 in vitro. Unlike wild type His-HSP90α, these HSP90 mutants did not enhance FN matrix assembly in the Hs578T cell line model when added exogenously. Interestingly, the HSP90 E353A mutation, which did not significantly reduce the HSP90 - FN interaction in vitro, dramatically blocked FN matrix assembly in Hs578T cell-derived matrices. Taken together, these data extend our understanding of the role of HSP90 in FN fibrillogenesis and suggest that promotion of FN ECM assembly by HSP90 is not solely regulated by the affinity of the direct interaction between HSP90 and FN.

Burkitt lymphoma (BL) is an aggressive B-cell lymphoma that remains a leading cause of childhood cancer mortality in sub-Saharan Africa. Although the epidemiological link between malaria and BL has been established, our understanding of the underlying immunological mechanisms conducive to tumorigenesis is incomplete. To address a noted gap in our knowledge of the immune landscape, we profiled neutrophil subsets from children with different exposure histories to -malaria and children diagnosed with BL from Western Kenya, along with healthy malaria-naive Kenyan adults. Using multiparameter flow cytometry, we characterized neutrophils by expression of CD15, CD16, CD10, CD11b, CD182, CD184, and CD62L and found that malaria-exposed children exhibited increased frequencies of aged neutrophil subsets, accompanied by a reduction in the mature subset frequencies compared to malaria-naive children. Malaria-exposed children also had neutrophil profiles that closely resembled those seen in the adults. Notably, a positive correlation (rs was observed in immature neutrophils between malaria-exposed healthy and BL children, indicating a similar expansion pattern of this subset in both groups. This finding suggests a malaria-driven expansion of the immature subset, potentially promoting a permissive environment for BL. Our data suggests that the observed shift in neutrophil profiles could contribute to the malaria-induced immunopathology associated with BL.

Background The emergence of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) has been the most significant public health challenge in over a century. SARS‐CoV‐2 has infected over 765 million people worldwide, resulting in over 6.9 million deaths. This study aimed to detect community transmission of SARS‐CoV‐2 and monitor the co‐circulation of SARS‐CoV‐2 with other acute respiratory pathogens in Rift Valley, Kenya. Methods We conducted a cross‐sectional active sentinel surveillance for the SARS‐CoV‐2 virus among patients with acute respiratory infections at four sites in Rift Valley from January 2022 to December 2022. One thousand two hundred seventy‐one patients aged between 3 years and 98 years presenting with influenza‐like illness (ILI) were recruited into the study. Nasopharyngeal swab specimens from all study participants were screened using a reverse transcription‐quantitative polymerase chain reaction (RT‐qPCR) for SARS‐CoV‐2, influenza A, influenza B and respiratory syncytial virus (RSV). Results The samples that tested positive for influenza A (n = 73) and RSV (n = 12) were subtyped, while SARS‐CoV‐2 (n = 177) positive samples were further screened for 12 viral and seven bacterial respiratory pathogens. We had a prevalence of 13.9% for SARS‐CoV‐2, 5.7% for influenza A, 2% for influenza B and 1% for RSV. Influenza A‐H1pdm09 and RSV B were the most dominant circulating subtypes of influenza A and RSV, respectively. The most common co‐infecting pathogens were Streptococcus pneumoniae (n = 29) and Haemophilus influenzae (n = 19), accounting for 16.4% and 10.7% of all the SARS‐CoV‐2 positive samples. Conclusions Augmenting syndromic testing in acute respiratory infections (ARIs) surveillance is crucial to inform evidence‐based clinical and public health interventions.