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1. The 2023 South Sudanese outbreak of Hepatitis E emphasizes ongoing circulation of genotype 1 in North, Central, and East Africa

Author

Orf, Gregory S., Bbosa, Nicholas, Berg, Michael G., Downing, Robert, Weiss, Sonja L., Ssemwanga, Deogratius, Ssekagiri, Alfred, Ashraf, Shirin, da Silva Filipe, Ana, Kiiza, Ronald, Buule, Joshua, Namagembe, Hamidah Suubi, Nabirye, Stella Esther, Kayiwa, John, Deng, Lul Lojok, Wani, Gregory, Maror, James A., Baguma, Andrew, Mogga, Juma J.H., Kamili, Saleem, Thomson, Emma C., Kaleebu, Pontiano, and Cloherty, Gavin A.

Published
2024
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2. Rapid Replacement of SARS-CoV-2 Variants by Delta and Subsequent Arrival of Omicron, Uganda, 2021

Author

Bbosa, Nicholas, Ssemwanga, Deogratius, Namagembe, Hamidah, Kiiza, Ronald, Kiconco, Jocelyn, Kayiwa, John, Lutalo, Tom, Lutwama, Julius, Ssekagiri, Alfred, Ssewanyana, Isaac, Nabadda, Susan, Kyobe-Bbosa, Henry, Giandhari, Jennifer, Pillay, Sureshnee, Ramphal, Upasana, Ramphal, Yajna, Naidoo, Yeshnee, Tshiabuila, Derek, Tegally, Houriiyah, San, Emmanuel J., Wilkinson, Eduan, de Oliveira, Tulio, and Kaleebu, Pontiano

Subjects
Epidemics -- Risk factors -- Causes of, Company distribution practices, Health
Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the etiologic agent of human coronavirus disease (COVID-19), which was declared by the World Health Organization to be a global pandemic in [...]

Published
2022
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3. Sequence Notes: Near Full-Length Genome Analysis of the First-Reported HIV-1 Circulating Recombinant Form (CRF)_10CD in Uganda.

Author

Bbosa, Nicholas, Holzmayer, Vera, Ssemwanga, Deogratius, Downing, Robert, Ssekagiri, Alfred, Anderson, Mark, Rodgers, Mary A., Kaleebu, Pontiano, and Cloherty, Gavin

Abstract

HIV-1 is characterized by remarkable genetic diversity resulting from its high replication rate, error-prone reverse transcriptase enzyme and recombination events. In Uganda, HIV-1 subtype diversity is mostly dominated by subtypes A, D, and A1/D Unique Recombinant Forms (URFs). In this study, deep sequences of HIV from patients with known antiretroviral therapy (ART) status were analyzed to determine the subtypes and to identify drug-resistance mutations circulating in the study population. Of the 187 participant samples processed for next-generation sequencing (NGS), 137 (73%) were successfully classified. The majority of HIV-1 strains were classified as subtype A (75, 55%), D (43, 31%), with other subtypes including C (3, 2%), A1/D (9, 7%) and CRF10_CD (1, <1%). Recombinant analysis of nine complete A1/D HIV genomes identified novel recombination patterns described herein. Furthermore, we report for the first time in Uganda, an HIV-1 CRF10_CD strain from a fisherfolk in a Lake Victoria Island fishing community. [ABSTRACT FROM AUTHOR]

Published
2024
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5. Next-Generation Sequencing Reveals a High Frequency of HIV-1 Minority Variants and an Expanded Drug Resistance Profile among Individuals on First-Line ART.

Author

Nannyonjo, Maria, Omooja, Jonah, Bugembe, Daniel Lule, Bbosa, Nicholas, Lunkuse, Sandra, Nabirye, Stella Esther, Nassolo, Faridah, Namagembe, Hamidah, Abaasa, Andrew, Kazibwe, Anne, Kaleebu, Pontiano, and Ssemwanga, Deogratius

Subjects
NUCLEOTIDE sequencing, DRUG utilization, DRUG resistance, ANTIRETROVIRAL agents, HIV
Abstract

We assessed the performance and clinical relevance of Illumina MiSeq next-generation sequencing (NGS) for HIV-1 genotyping compared with Sanger sequencing (SS). We analyzed 167 participants, 45 with virologic failure (VL ≥ 1000 copies/mL), i.e., cases, and 122 time-matched participants with virologic suppression (VL < 1000 copies/mL), i.e., controls, 12 months post-ART initiation. Major surveillance drug resistance mutations (SDRMs) detected by SS were all detectable by NGS. Among cases at 12 months, SS identified SDRMs in 32/45 (71.1%) while NGS identified SDRMs among 35/45 (77.8%), increasing the number of cases with SDRMs by 3/45 (6.7%). Participants identified with, and proportions of major SDRMs increased when NGS was used. NGS vs. SS at endpoint revealed for NNRTIs: 36/45 vs. 33/45; Y181C: 26/45 vs. 24/45; K103N: 9/45 vs. 6/45 participants with SDRMs, respectively. At baseline, NGS revealed major SDRMs in 9/45 (20%) cases without SDRMs by SS. Participant MBL/043, among the nine, the following major SDRMs existed: L90M to PIs, K65R and M184V to NRTIs, and Y181C and K103N to NNRTIs. The SDRMs among the nine increased SDRMs to NRTIs, NNRTIs, and PIs. Only 43/122 (25.7%) of participants had pre-treatment minority SDRMs. Also, 24.4% of the cases vs. 26.2 of controls had minority SDRMs (p = 0.802); minority SDRMs were not associated with virologic failure. NGS agreed with SS in HIV-1 genotyping but detected additional major SDRMs and identified more participants harboring major SDRMs, expanding the HIV DRM profile of this cohort. NGS could improve HIV genotyping to guide treatment decisions for enhancing ART efficacy, a cardinal pre-requisite in the pursuit of the UNAIDS 95-95-95 targets. [ABSTRACT FROM AUTHOR]

Published
2024
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7. Geographic and population distributions of HIV-1 and HIV-2 circulating subtypes: a systematic literature review and meta-analysis (2010-2021)

Author

Williams, Alexandria, primary, Menon, Sonia, additional, Crowe, Madeleine, additional, Agarwal, Neha, additional, Biccler, Jorne, additional, Bbosa, Nicholas, additional, Ssemwanga, Deogratius, additional, Adungo, Ferdinard, additional, Moecklinghoff, Christiane, additional, Macartney, Malcolm, additional, and Oriol-Mathieu, Valerie, additional

Published
2023
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10. Geographic and Population Distributions of Human Immunodeficiency Virus (HIV)–1 and HIV-2 Circulating Subtypes: A Systematic Literature Review and Meta-analysis (2010–2021).

Author

Williams, Alexandria, Menon, Sonia, Crowe, Madeleine, Agarwal, Neha, Biccler, Jorne, Bbosa, Nicholas, Ssemwanga, Deogratius, Adungo, Ferdinard, Moecklinghoff, Christiane, Macartney, Malcolm, and Oriol-Mathieu, Valerie

Subjects
HIV, HIV infections, HIV seroconversion, GENETIC recombination, VACCINE development
Abstract

Background HIV poses significant challenges for vaccine development due to its high genetic mutation and recombination rates. Understanding the distribution of HIV subtypes (clades) across regions and populations is crucial. In this study, a systematic review of the past decade was conducted to characterize HIV-1/HIV-2 subtypes. Methods A comprehensive search was performed in PubMed, EMBASE, and CABI Global Health, yielding 454 studies from 91 countries. Results Globally, circulating recombinant forms (CRFs)/unique recombinant forms (URFs) accounted for 29% of HIV-1 strains, followed by subtype C (23%) and subtype A (17%). Among studies reporting subtype breakdowns in key populations, 62% of HIV infections among men who have sex with men (MSM) and 38% among people who inject drugs (PWIDs) were CRF/URFs. Latin America and the Caribbean exhibited a 25% increase in other CRFs (excluding CRF01_AE or CRF02_AG) prevalence between 2010–2015 and 2016–2021. Conclusions This review underscores the global distribution of HIV subtypes, with an increasing prevalence of CRFs and a lower prevalence of subtype C. Data on HIV-2 were limited. Understanding subtype diversity is crucial for vaccine development, which need to elicit immune responses capable of targeting various subtypes. Further research is needed to enhance our knowledge and address the challenges posed by HIV subtype diversity. [ABSTRACT FROM AUTHOR]

Published
2023
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11. The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

Author

Tegally, Houriiyah, primary, San, James E., additional, Cotten, Matthew, additional, Tegomoh, Bryan, additional, Mboowa, Gerald, additional, Martin, Darren P., additional, Baxter, Cheryl, additional, Moir, Monika, additional, Lambisia, Arnold, additional, Diallo, Amadou, additional, Amoako, Daniel G., additional, Diagne, Moussa M., additional, Sisay, Abay, additional, Zekri, Abdel-Rahman N., additional, Barakat, Abdelhamid, additional, Gueye, Abdou Salam, additional, Sangare, Abdoul K., additional, Ouedraogo, Abdoul-Salam, additional, Sow, Abdourahmane, additional, Musa, Abdualmoniem O., additional, Sesay, Abdul K., additional, Lagare, Adamou, additional, Kemi, Adedotun-Sulaiman, additional, Abar, Aden Elmi, additional, Johnson, Adeniji A., additional, Fowotade, Adeola, additional, Olubusuyi, Adewumi M., additional, Oluwapelumi, Adeyemi O., additional, Amuri, Adrienne A., additional, Juru, Agnes, additional, Ramadan, Ahmad Mabrouk, additional, Kandeil, Ahmed, additional, Mostafa, Ahmed, additional, Rebai, Ahmed, additional, Sayed, Ahmed, additional, Kazeem, Akano, additional, Balde, Aladje, additional, Christoffels, Alan, additional, Trotter, Alexander J., additional, Campbell, Allan, additional, Keita, Alpha Kabinet, additional, Kone, Amadou, additional, Bouzid, Amal, additional, Souissi, Amal, additional, Agweyu, Ambrose, additional, Gutierrez, Ana V., additional, Page, Andrew J., additional, Yadouleton, Anges, additional, Vinze, Anika, additional, Happi, Anise N., additional, Chouikha, Anissa, additional, Iranzadeh, Arash, additional, Maharaj, Arisha, additional, Batchi-Bouyou, Armel Landry, additional, Ismail, Arshad, additional, Sylverken, Augustina, additional, Goba, Augustine, additional, Femi, Ayoade, additional, Sijuwola, Ayotunde Elijah, additional, Ibrahimi, Azeddine, additional, Marycelin, Baba, additional, Salako, Babatunde Lawal, additional, Oderinde, Bamidele S., additional, Bolajoko, Bankole, additional, Dhaala, Beatrice, additional, Herring, Belinda L., additional, Tsofa, Benjamin, additional, Mvula, Bernard, additional, Njanpop-Lafourcade, Berthe-Marie, additional, Marondera, Blessing T., additional, Khaireh, Bouh Abdi, additional, Kouriba, Bourema, additional, Adu, Bright, additional, Pool, Brigitte, additional, McInnis, Bronwyn, additional, Brook, Cara, additional, Williamson, Carolyn, additional, Anscombe, Catherine, additional, Pratt, Catherine B., additional, Scheepers, Cathrine, additional, Akoua-Koffi, Chantal G., additional, Agoti, Charles N., additional, Loucoubar, Cheikh, additional, Onwuamah, Chika Kingsley, additional, Ihekweazu, Chikwe, additional, Malaka, Christian Noël, additional, Peyrefitte, Christophe, additional, Omoruyi, Chukwuma Ewean, additional, Rafaï, Clotaire Donatien, additional, Morang’a, Collins M., additional, Nokes, D. James, additional, Lule, Daniel Bugembe, additional, Bridges, Daniel J., additional, Mukadi-Bamuleka, Daniel, additional, Park, Danny, additional, Baker, David, additional, Doolabh, Deelan, additional, Ssemwanga, Deogratius, additional, Tshiabuila, Derek, additional, Bassirou, Diarra, additional, Amuzu, Dominic S.Y., additional, Goedhals, Dominique, additional, Grant, Donald S., additional, Omuoyo, Donwilliams O., additional, Maruapula, Dorcas, additional, Wanjohi, Dorcas Waruguru, additional, Foster-Nyarko, Ebenezer, additional, Lusamaki, Eddy K., additional, Simulundu, Edgar, additional, Ong’era, Edidah M., additional, Ngabana, Edith N., additional, Abworo, Edward O., additional, Otieno, Edward, additional, Shumba, Edwin, additional, Barasa, Edwine, additional, Ahmed, El Bara, additional, Kampira, Elizabeth, additional, Fahime, Elmostafa El, additional, Lokilo, Emmanuel, additional, Mukantwari, Enatha, additional, Cyril, Erameh, additional, Philomena, Eromon, additional, Belarbi, Essia, additional, Simon-Loriere, Etienne, additional, Anoh, Etilé A., additional, Leendertz, Fabian, additional, Taweh, Fahn M., additional, Wasfi, Fares, additional, Abdelmoula, Fatma, additional, Takawira, Faustinos T., additional, Derrar, Fawzi, additional, Ajogbasile, Fehintola V, additional, Treurnicht, Florette, additional, Onikepe, Folarin, additional, Ntoumi, Francine, additional, Muyembe, Francisca M., additional, Ngiambudulu, Francisco, additional, Zongo Ragomzingba, Frank Edgard, additional, Dratibi, Fred Athanasius, additional, Iyanu, Fred-Akintunwa, additional, Mbunsu, Gabriel K., additional, Thilliez, Gaetan, additional, Kay, Gemma L., additional, Akpede, George O., additional, George, Uwem E., additional, van Zyl, Gert, additional, Awandare, Gordon A., additional, Schubert, Grit, additional, Maphalala, Gugu P., additional, Ranaivoson, Hafaliana C., additional, Lemriss, Hajar, additional, Omunakwe, Hannah E, additional, Onywera, Harris, additional, Abe, Haruka, additional, Karray, Hela, additional, Nansumba, Hellen, additional, Triki, Henda, additional, Adje Kadjo, Herve Albéric, additional, Elgahzaly, Hesham, additional, Gumbo, Hlanai, additional, mathieu, Hota, additional, Kavunga-Membo, Hugo, additional, Smeti, Ibtihel, additional, Olawoye, Idowu B., additional, Adetifa, Ifedayo, additional, Odia, Ikponmwosa, additional, Boubaker, Ilhem Boutiba-Ben, additional, Ssewanyana, Isaac, additional, Wurie, Isatta, additional, Konstantinus, Iyaloo S, additional, Afiwa Halatoko, Jacqueline Wemboo, additional, Ayei, James, additional, Sonoo, Janaki, additional, Lekana-Douki, Jean Bernard, additional, Makangara, Jean-Claude C., additional, Tamfum, Jean-Jacques M., additional, Heraud, Jean-Michel, additional, Shaffer, Jeffrey G., additional, Giandhari, Jennifer, additional, Musyoki, Jennifer, additional, Uwanibe, Jessica N., additional, Bhiman, Jinal N., additional, Yasuda, Jiro, additional, Morais, Joana, additional, Mends, Joana Q., additional, Kiconco, Jocelyn, additional, Sandi, John Demby, additional, Huddleston, John, additional, Odoom, John Kofi, additional, Morobe, John M., additional, Gyapong, John O., additional, Kayiwa, John T., additional, Okolie, Johnson C., additional, Xavier, Joicymara Santos, additional, Gyamfi, Jones, additional, Kofi Bonney, Joseph Humphrey, additional, Nyandwi, Joseph, additional, Everatt, Josie, additional, Farah, Jouali, additional, Nakaseegu, Joweria, additional, Ngoi, Joyce M., additional, Namulondo, Joyce, additional, Oguzie, Judith U., additional, Andeko, Julia C., additional, Lutwama, Julius J., additional, O’Grady, Justin, additional, Siddle, Katherine J, additional, Victoir, Kathleen, additional, Adeyemi, Kayode T., additional, Tumedi, Kefentse A., additional, Carvalho, Kevin Sanders, additional, Mohammed, Khadija Said, additional, Musonda, Kunda G., additional, Duedu, Kwabena O., additional, Belyamani, Lahcen, additional, Fki-Berrajah, Lamia, additional, Singh, Lavanya, additional, Biscornet, Leon, additional, de Oliveira Martins, Leonardo, additional, Chabuka, Lucious, additional, Olubayo, Luicer, additional, Deng, Lul Lojok, additional, Ochola-Oyier, Lynette Isabella, additional, Mine, Madisa, additional, Ramuth, Magalutcheemee, additional, Mastouri, Maha, additional, ElHefnawi, Mahmoud, additional, Mbanne, Maimouna, additional, Matsheka, Maitshwarelo I., additional, Kebabonye, Malebogo, additional, Diop, Mamadou, additional, Momoh, Mambu, additional, Lima Mendonça, Maria da Luz, additional, Venter, Marietjie, additional, Paye, Marietou F, additional, Faye, Martin, additional, Nyaga, Martin M., additional, Mareka, Mathabo, additional, Damaris, Matoke-Muhia, additional, Mburu, Maureen W., additional, Mpina, Maximillian, additional, Claujens Chastel, Mfoutou Mapanguy, additional, Owusu, Michael, additional, Wiley, Michael R., additional, Tatfeng, Mirabeau Youtchou, additional, Ayekaba, Mitoha Ondo’o, additional, Abouelhoda, Mohamed, additional, Beloufa, Mohamed Amine, additional, Seadawy, Mohamed G, additional, Khalifa, Mohamed K., additional, Dellagi, Mohammed Koussai, additional, Matobo, Mooko Marethabile, additional, Kane, Mouhamed, additional, Ouadghiri, Mouna, additional, Salou, Mounerou, additional, Mbulawa, Mphaphi B., additional, Saibu, Mudashiru Femi, additional, Mwenda, Mulenga, additional, Kaba, Muluken, additional, Phan, My V.T., additional, Abid, Nabil, additional, Touil, Nadia, additional, Rujeni, Nadine, additional, Ismael, Nalia, additional, Top, Ndeye Marieme, additional, Dia, Ndongo, additional, Mabunda, Nédio, additional, Hsiao, Nei-yuan, additional, Silochi, Nelson Boricó, additional, Saasa, Ngonda, additional, Bbosa, Nicholas, additional, Murunga, Nickson, additional, Gumede, Nicksy, additional, Wolter, Nicole, additional, Sitharam, Nikita, additional, Ndodo, Nnaemeka, additional, Ajayi, Nnennaya A., additional, Tordo, Noël, additional, Mbhele, Nokuzola, additional, Razanajatovo, Norosoa H, additional, Iguosadolo, Nosamiefan, additional, Mba, Nwando, additional, Kingsley, Ojide C., additional, Sylvanus, Okogbenin, additional, Peter, Okokhere, additional, Femi, Oladiji, additional, Testimony, Olumade, additional, Ogunsanya, Olusola Akinola, additional, Fakayode, Oluwatosin, additional, Ogah, Onwe E., additional, Faye, Ousmane, additional, Smith-Lawrence, Pamela, additional, Ondoa, Pascale, additional, Combe, Patrice, additional, Nabisubi, Patricia, additional, Semanda, Patrick, additional, Oluniyi, Paul E., additional, Arnaldo, Paulo, additional, Quashie, Peter Kojo, additional, Bejon, Philip, additional, Dussart, Philippe, additional, Bester, Phillip A., additional, Mbala, Placide K., additional, Kaleebu, Pontiano, additional, Abechi, Priscilla, additional, El-Shesheny, Rabeh, additional, Joseph, Rageema, additional, Aziz, Ramy Karam, additional, Essomba, René Ghislain, additional, Ayivor-Djanie, Reuben, additional, Njouom, Richard, additional, Phillips, Richard O., additional, Gorman, Richmond, additional, Kingsley, Robert A., additional, Audu, Rosemary, additional, Carr, Rosina A.A., additional, Kabbaj, Saâd El, additional, Gargouri, Saba, additional, Masmoudi, Saber, additional, Sankhe, Safietou, additional, Mohamed, Sahra Isse, additional, Mhalla, Salma, additional, Hosch, Salome, additional, Kassim, Samar Kamal, additional, Metha, Samar, additional, Trabelsi, Sameh, additional, Lemriss, Sanaâ, additional, Agwa, Sara Hassan, additional, Mwangi, Sarah Wambui, additional, Doumbia, Seydou, additional, Makiala-Mandanda, Sheila, additional, Aryeetey, Sherihane, additional, Ahmed, Shymaa S., additional, Ahmed, Sidi Mohamed, additional, Elhamoumi, Siham, additional, Moyo, Sikhulile, additional, Lutucuta, Silvia, additional, Gaseitsiwe, Simani, additional, Jalloh, Simbirie, additional, Andriamandimby, Soafy, additional, Oguntope, Sobajo, additional, Grayo, Solène, additional, Lekana-Douki, Sonia, additional, Prosolek, Sophie, additional, Ouangraoua, Soumeya, additional, van Wyk, Stephanie, additional, Schaffner, Stephen F., additional, Kanyerezi, Stephen, additional, Ahuka-Mundeke, Steve, additional, Rudder, Steven, additional, Pillay, Sureshnee, additional, Nabadda, Susan, additional, Behillil, Sylvie, additional, Budiaki, Sylvie L., additional, van der Werf, Sylvie, additional, Mashe, Tapfumanei, additional, Aanniz, Tarik, additional, Mohale, Thabo, additional, Le-Viet, Thanh, additional, Velavan, Thirumalaisamy P., additional, Schindler, Tobias, additional, Maponga, Tongai, additional, Bedford, Trevor, additional, Anyaneji, Ugochukwu J., additional, Chinedu, Ugwu, additional, Ramphal, Upasana, additional, Enouf, Vincent, additional, Nene, Vishvanath, additional, Gorova, Vivianne, additional, Roshdy, Wael H., additional, Karim, Wasim Abdul, additional, Ampofo, William K., additional, Preiser, Wolfgang, additional, Choga, Wonderful T., additional, Ahmed, Yahaya Ali, additional, Ramphal, Yajna, additional, Bediako, Yaw, additional, Naidoo, Yeshnee, additional, Butera, Yvan, additional, de Laurent, Zaydah R., additional, Ouma, Ahmed E.O., additional, von Gottberg, Anne, additional, Githinji, George, additional, Moeti, Matshidiso, additional, Tomori, Oyewale, additional, Sabeti, Pardis C., additional, Sall, Amadou A., additional, Oyola, Samuel O., additional, Tebeje, Yenew K., additional, Tessema, Sofonias K., additional, de Oliveira, Tulio, additional, Happi, Christian, additional, Lessells, Richard, additional, Nkengasong, John, additional, and Wilkinson, Eduan, additional

Published
2022
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12. HIV-1 drug resistance genotyping success rates and correlates of Dried-blood spots and plasma specimen genotyping failure in a resource-limited setting

Author

Omooja, Jonah, primary, Bbosa, Nicholas, additional, Lule, Dan Bugembe, additional, Nannyonjo, Maria, additional, Lunkuse, Sandra, additional, Nassolo, Faridah, additional, Nabirye, Stella, additional, Suubi, Hamidah Namagembe, additional, Kaleebu, Pontiano, additional, and Ssemwanga, Deogratius, additional

Published
2022
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15. Phylogenetic Networks and Parameters Inferred from HIV Nucleotide Sequences of High-Risk and General Population Groups in Uganda: Implications for Epidemic Control

Author

Bbosa, Nicholas, primary, Ssemwanga, Deogratius, additional, Nsubuga, Rebecca N., additional, Kiwanuka, Noah, additional, Bagaya, Bernard S., additional, Kitayimbwa, John M., additional, Ssekagiri, Alfred, additional, Yebra, Gonzalo, additional, Kaleebu, Pontiano, additional, and Leigh-Brown, Andrew, additional

Published
2021
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16. Short Communication: Choosing the Right Program for the Identification of HIV-1 Transmission Networks from Nucleotide Sequences Sampled from Different Populations

Author

Bbosa, Nicholas, Ssemwanga, Deogratius, and Kaleebu, Pontiano

Abstract

HIV-TRAnsmission Cluster Engine (HIV-TRACE) and Cluster Picker are some of the most widely used programs for identifying HIV-1 transmission networks from nucleotide sequences. However, choosing between these tools is subjective and often a matter of personal preference. Because these software use different algorithms to detect HIV-1 transmission networks, their optimal use is better suited with different sequence data sets and under different scenarios. The performance of these tools has previously been evaluated across a range of genetic distance thresholds without an assessment of the differences in the structure of networks identified. In this study, we tested both programs on the same HIV-1 pol sequence data set (n?=?2,017) from three Ugandan populations to examine their performance across different risk groups and evaluate the structure of networks identified. HIV-TRACE that uses a single-linkage algorithm identified more nodes in the same networks that were connected by sparse links than Cluster Picker. This suggests that the choice of the program used for identifying networks should depend on the study aims, the characteristics of the population being investigated, dynamics of the epidemic, sampling design, and the nature of research questions being addressed for optimum results. HIV-TRACE could be more applicable with larger data sets where the aim is to identify larger clusters that represent distinct transmission chains and in more diverse populations where infection has occurred over a period of time. In contrast, Cluster Picker is applicable in situations where more closely connected clusters are expected in the studied populations.

Published
2020

17. High Levels of Acquired HIV Drug Resistance Following Virological Nonsuppression in HIV-Infected Women from a High-Risk Cohort in Uganda

Author

Segujja, Farouk, Omooja, Jonah, Lunkuse, Sandra, Nanyonjo, Maria, Nabirye, Stella E, Nassolo, Faridah, Bugembe, Daniel L, Bbosa, Nicholas, Kateete, David P, Ssenyonga, William, Mayanja, Yunia, Nsubuga, Rebecca N, Seeley, Janet, Kaleebu, Pontiano, and Ssemwanga, Deogratius

Abstract

HIV drug resistance (HIVDR) is of increasing health concern, especially among key populations. We investigated the prevalence of virological suppression (VS), prevalence and correlates of HIVDR in HIV-infected women, enrolled in a high-risk cohort. We enrolled 267 women initiated on first-line antiretroviral therapy (ART) between 2015 and 2018. Participants' plasma samples were analyzed for HIV RNA viral load (VL) and genotypic resistance testing was performed on those with VL nonsuppression (defined as VL ≥1,000 copies/mL). We used the Stanford HIVDR database-algorithm to assess HIVDR mutations and logistic regression to assess risk factors for VL nonsuppression and HIVDR. We observed an overall VS prevalence of 76.0% (203/267) and detected respective acquired drug resistance prevalence to non-nucleoside reverse transcriptase inhibitors (NNRTIs) and nucleoside reverse transcriptase inhibitors (NRTIs) of 81.3% [confidence interval (CI) 67.4-91.1] and 45.8% (CI 31.4-60.8) among the 48 successfully genotyped VL nonsuppressors. NNRTI mutations were observed in 81.3% (39/48) of the genotyped participants and 45.8% (22/48) had both NRTI and NNRTI mutations. The mutation K103N was detected in 62.5% (30/48) of participants, 41.7% (20/48) had M184V/I, 14.6% had K65R, and 12.5% (6/48) had thymidine analog mutations (TAMs). None of the analyzed potential risk factors, including age and duration on ART, was significantly correlated with VL nonsuppression or HIVDR. Although high levels of NNRTI mutations support the transition to dolutegravir, the presence of NRTI mutations, especially TAMs, may compromise dolutegravir-based regimens or other second-line ART options. The moderate VS prevalence and high HIVDR prevalence therefore call for timely ART switching and intensive adherence counseling.

Published
2020

18. The Molecular Epidemiology and Transmission Dynamics of HIV Type 1 in a General Population Cohort in Uganda

Author

Ssemwanga, Deogratius, primary, Bbosa, Nicholas, additional, Nsubuga, Rebecca N., additional, Ssekagiri, Alfred, additional, Kapaata, Anne, additional, Nannyonjo, Maria, additional, Nassolo, Faridah, additional, Karabarinde, Alex, additional, Mugisha, Joseph, additional, Seeley, Janet, additional, Yebra, Gonzalo, additional, Leigh Brown, Andrew, additional, and Kaleebu, Pontiano, additional

Published
2020
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19. Pervasive and non-random recombination in near full-length HIV genomes from Uganda

Author

Grant, Heather E, primary, Hodcroft, Emma B, primary, Ssemwanga, Deogratius, primary, Kitayimbwa, John M, primary, Yebra, Gonzalo, primary, Esquivel Gomez, Luis Roger, primary, Frampton, Dan, primary, Gall, Astrid, primary, Kellam, Paul, primary, de Oliveira, Tulio, primary, Bbosa, Nicholas, primary, Nsubuga, Rebecca N, primary, Kibengo, Freddie, primary, Kwan, Tsz Ho, primary, Lycett, Samantha, primary, Kao, Rowland, primary, Robertson, David L, primary, Ratmann, Oliver, primary, Fraser, Christophe, primary, Pillay, Deenan, primary, Kaleebu, Pontiano, primary, and Leigh Brown, Andrew J, primary

Published
2020
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20. Rates of HIV-1 virological suppression and patterns of acquired drug resistance among fisherfolk on first-line antiretroviral therapy in Uganda

Author

Omooja, Jonah, primary, Nannyonjo, Maria, additional, Sanyu, Grace, additional, Nabirye, Stella E, additional, Nassolo, Faridah, additional, Lunkuse, Sandra, additional, Kapaata, Anne, additional, Segujja, Farouk, additional, Kateete, David Patrick, additional, Ssebaggala, Eric, additional, Bbosa, Nicholas, additional, Aling, Emmanuel, additional, Nsubuga, Rebecca N, additional, Kaleebu, Pontiano, additional, and Ssemwanga, Deogratius, additional

Published
2019
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21. Phylogenetic and Demographic Characterization of Directed HIV-1 Transmission Using Deep Sequences from High-Risk and General Population Cohorts/Groups in Uganda.

Author

Bbosa, Nicholas, Ssemwanga, Deogratius, Ssekagiri, Alfred, Xi, Xiaoyue, Mayanja, Yunia, Bahemuka, Ubaldo, Seeley, Janet, Pillay, Deenan, Abeler-Dörner, Lucie, Golubchik, Tanya, Fraser, Christophe, Kaleebu, Pontiano, and Ratmann, Oliver

Subjects
*POPULATION, *VIRAL load, *FISHING villages, *GAY couples, *ANTIRETROVIRAL agents
Abstract

Across sub-Saharan Africa, key populations with elevated HIV-1 incidence and/or prevalence have been identified, but their contribution to disease spread remains unclear. We performed viral deep-sequence phylogenetic analyses to quantify transmission dynamics between the general population (GP), fisherfolk communities (FF), and women at high risk of infection and their clients (WHR) in central and southwestern Uganda. Between August 2014 and August 2017, 6185 HIV-1 positive individuals were enrolled in 3 GP and 10 FF communities, 3 WHR enrollment sites. A total of 2531 antiretroviral therapy (ART) naïve participants with plasma viral load >1000 copies/mL were deep-sequenced. One hundred and twenty-three transmission networks were reconstructed, including 105 phylogenetically highly supported source–recipient pairs. Only one pair involved a WHR and male participant, suggesting that improved population sampling is needed to assess empirically the role of WHR to the transmission dynamics. More transmissions were observed from the GP communities to FF communities than vice versa, with an estimated flow ratio of 1.56 (95% CrI 0.68–3.72), indicating that fishing communities on Lake Victoria are not a net source of transmission flow to neighboring communities further inland. Men contributed disproportionally to HIV-1 transmission flow regardless of age, suggesting that prevention efforts need to better aid men to engage with and stay in care. [ABSTRACT FROM AUTHOR]

Published
2020
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22. Case Reports of Human Monkeypox Virus Infections, Uganda, 2024.

Author

Bbosa N, Nabirye SE, Namagembe HS, Kiiza R, Ssekagiri A, Munyagwa M, Bwambale A, Bagonza S, Bosa HK, Downing R, Lutwama J, Kaleebu P, and Ssemwanga D

Abstract

Mpox is a zoonotic disease caused by the monkeypox virus. We report on human mpox cases in Uganda identified by PCR and confirmed by deep sequencing. Phylogenetic analysis revealed clustering with other clade Ib sequences associated with recent outbreaks in the Democratic Republic of the Congo.

Published
2024
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23. HIVseqDB: a portable resource for NGS and sample metadata integration for HIV-1 drug resistance analysis.

Author

Ssekagiri A, Jjingo D, Bbosa N, Bugembe DL, Kateete DP, Jordan IK, Kaleebu P, and Ssemwanga D

Abstract

Summary: Human immunodeficiency virus (HIV) remains a public health threat, with drug resistance being a major concern in HIV treatment. Next-generation sequencing (NGS) is a powerful tool for identifying low-abundance drug resistance mutations (LA-DRMs) that conventional Sanger sequencing cannot reliably detect. To fully understand the significance of LA-DRMs, it is necessary to integrate NGS data with clinical and demographic data. However, freely available tools for NGS-based HIV-1 drug resistance analysis do not integrate these data. This poses a challenge in interpretation of the impact of LA-DRMs, mainly for resource-limited settings due to the shortage of bioinformatics expertise. To address this challenge, we present HIVseqDB, a portable, secure, and user-friendly resource for integrating NGS data with associated clinical and demographic data for analysis of HIV drug resistance. HIVseqDB currently supports uploading of NGS data and associated sample data, HIV-1 drug resistance data analysis, browsing of uploaded data, and browsing and visualizing of analysis results. Each function of HIVseqDB corresponds to an individual Django application. This ensures efficient incorporation of additional features with minimal effort. HIVseqDB can be deployed on various computing environments, such as on-premises high-performance computing facilities and cloud-based platforms., Availability and Implementation: HIVseqDB is available at https://github.com/AlfredUg/HIVseqDB. A deployed instance of HIVseqDB is available at https://hivseqdb.org., Competing Interests: None declared., (© The Author(s) 2024. Published by Oxford University Press.)

Published
2024
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24. The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance.

Author

Tegally H, San JE, Cotten M, Moir M, Tegomoh B, Mboowa G, Martin DP, Baxter C, Lambisia AW, Diallo A, Amoako DG, Diagne MM, Sisay A, Zekri AN, Gueye AS, Sangare AK, Ouedraogo AS, Sow A, Musa AO, Sesay AK, Abias AG, Elzagheid AI, Lagare A, Kemi AS, Abar AE, Johnson AA, Fowotade A, Oluwapelumi AO, Amuri AA, Juru A, Kandeil A, Mostafa A, Rebai A, Sayed A, Kazeem A, Balde A, Christoffels A, Trotter AJ, Campbell A, Keita AK, Kone A, Bouzid A, Souissi A, Agweyu A, Naguib A, Gutierrez AV, Nkeshimana A, Page AJ, Yadouleton A, Vinze A, Happi AN, Chouikha A, Iranzadeh A, Maharaj A, Batchi-Bouyou AL, Ismail A, Sylverken AA, Goba A, Femi A, Sijuwola AE, Marycelin B, Salako BL, Oderinde BS, Bolajoko B, Diarra B, Herring BL, Tsofa B, Lekana-Douki B, Mvula B, Njanpop-Lafourcade BM, Marondera BT, Khaireh BA, Kouriba B, Adu B, Pool B, McInnis B, Brook C, Williamson C, Nduwimana C, Anscombe C, Pratt CB, Scheepers C, Akoua-Koffi CG, Agoti CN, Mapanguy CM, Loucoubar C, Onwuamah CK, Ihekweazu C, Malaka CN, Peyrefitte C, Grace C, Omoruyi CE, Rafaï CD, Morang'a CM, Erameh C, Lule DB, Bridges DJ, Mukadi-Bamuleka D, Park D, Rasmussen DA, Baker D, Nokes DJ, Ssemwanga D, Tshiabuila D, Amuzu DSY, Goedhals D, Grant DS, Omuoyo DO, Maruapula D, Wanjohi DW, Foster-Nyarko E, Lusamaki EK, Simulundu E, Ong'era EM, Ngabana EN, Abworo EO, Otieno E, Shumba E, Barasa E, Ahmed EB, Ahmed EA, Lokilo E, Mukantwari E, Philomena E, Belarbi E, Simon-Loriere E, Anoh EA, Manuel E, Leendertz F, Taweh FM, Wasfi F, Abdelmoula F, Takawira FT, Derrar F, Ajogbasile FV, Treurnicht F, Onikepe F, Ntoumi F, Muyembe FM, Ragomzingba FEZ, Dratibi FA, Iyanu FA, Mbunsu GK, Thilliez G, Kay GL, Akpede GO, van Zyl GU, Awandare GA, Kpeli GS, Schubert G, Maphalala GP, Ranaivoson HC, Omunakwe HE, Onywera H, Abe H, Karray H, Nansumba H, Triki H, Kadjo HAA, Elgahzaly H, Gumbo H, Mathieu H, Kavunga-Membo H, Smeti I, Olawoye IB, Adetifa IMO, Odia I, Ben Boubaker IB, Muhammad IA, Ssewanyana I, Wurie I, Konstantinus IS, Halatoko JWA, Ayei J, Sonoo J, Makangara JC, Tamfum JM, Heraud JM, Shaffer JG, Giandhari J, Musyoki J, Nkurunziza J, Uwanibe JN, Bhiman JN, Yasuda J, Morais J, Kiconco J, Sandi JD, Huddleston J, Odoom JK, Morobe JM, Gyapong JO, Kayiwa JT, Okolie JC, Xavier JS, Gyamfi J, Wamala JF, Bonney JHK, Nyandwi J, Everatt J, Nakaseegu J, Ngoi JM, Namulondo J, Oguzie JU, Andeko JC, Lutwama JJ, Mogga JJH, O'Grady J, Siddle KJ, Victoir K, Adeyemi KT, Tumedi KA, Carvalho KS, Mohammed KS, Dellagi K, Musonda KG, Duedu KO, Fki-Berrajah L, Singh L, Kepler LM, Biscornet L, de Oliveira Martins L, Chabuka L, Olubayo L, Ojok LD, Deng LL, Ochola-Oyier LI, Tyers L, Mine M, Ramuth M, Mastouri M, ElHefnawi M, Mbanne M, Matsheka MI, Kebabonye M, Diop M, Momoh M, Lima Mendonça MDL, Venter M, Paye MF, Faye M, Nyaga MM, Mareka M, Damaris MM, Mburu MW, Mpina MG, Owusu M, Wiley MR, Tatfeng MY, Ayekaba MO, Abouelhoda M, Beloufa MA, Seadawy MG, Khalifa MK, Matobo MM, Kane M, Salou M, Mbulawa MB, Mwenda M, Allam M, Phan MVT, Abid N, Rujeni N, Abuzaid N, Ismael N, Elguindy N, Top NM, Dia N, Mabunda N, Hsiao NY, Silochi NB, Francisco NM, Saasa N, Bbosa N, Murunga N, Gumede N, Wolter N, Sitharam N, Ndodo N, Ajayi NA, Tordo N, Mbhele N, Razanajatovo NH, Iguosadolo N, Mba N, Kingsley OC, Sylvanus O, Femi O, Adewumi OM, Testimony O, Ogunsanya OA, Fakayode O, Ogah OE, Oludayo OE, Faye O, Smith-Lawrence P, Ondoa P, Combe P, Nabisubi P, Semanda P, Oluniyi PE, Arnaldo P, Quashie PK, Okokhere PO, Bejon P, Dussart P, Bester PA, Mbala PK, Kaleebu P, Abechi P, El-Shesheny R, Joseph R, Aziz RK, Essomba RG, Ayivor-Djanie R, Njouom R, Phillips RO, Gorman R, Kingsley RA, Neto Rodrigues RMDESA, Audu RA, Carr RAA, Gargouri S, Masmoudi S, Bootsma S, Sankhe S, Mohamed SI, Femi S, Mhalla S, Hosch S, Kassim SK, Metha S, Trabelsi S, Agwa SH, Mwangi SW, Doumbia S, Makiala-Mandanda S, Aryeetey S, Ahmed SS, Ahmed SM, Elhamoumi S, Moyo S, Lutucuta S, Gaseitsiwe S, Jalloh S, Andriamandimby SF, Oguntope S, Grayo S, Lekana-Douki S, Prosolek S, Ouangraoua S, van Wyk S, Schaffner SF, Kanyerezi S, Ahuka-Mundeke S, Rudder S, Pillay S, Nabadda S, Behillil S, Budiaki SL, van der Werf S, Mashe T, Mohale T, Le-Viet T, Velavan TP, Schindler T, Maponga TG, Bedford T, Anyaneji UJ, Chinedu U, Ramphal U, George UE, Enouf V, Nene V, Gorova V, Roshdy WH, Karim WA, Ampofo WK, Preiser W, Choga WT, Ahmed YA, Ramphal Y, Bediako Y, Naidoo Y, Butera Y, de Laurent ZR, Ouma AEO, von Gottberg A, Githinji G, Moeti M, Tomori O, Sabeti PC, Sall AA, Oyola SO, Tebeje YK, Tessema SK, de Oliveira T, Happi C, Lessells R, Nkengasong J, and Wilkinson E

Subjects
Africa epidemiology, Genomics, Humans, COVID-19 epidemiology, COVID-19 virology, Epidemiological Monitoring, Pandemics, SARS-CoV-2 genetics
Abstract

Investment in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing in Africa over the past year has led to a major increase in the number of sequences that have been generated and used to track the pandemic on the continent, a number that now exceeds 100,000 genomes. Our results show an increase in the number of African countries that are able to sequence domestically and highlight that local sequencing enables faster turnaround times and more-regular routine surveillance. Despite limitations of low testing proportions, findings from this genomic surveillance study underscore the heterogeneous nature of the pandemic and illuminate the distinct dispersal dynamics of variants of concern-particularly Alpha, Beta, Delta, and Omicron-on the continent. Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve while the continent faces many emerging and reemerging infectious disease threats. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century.

Published
2022
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25. Short Communication: Choosing the Right Program for the Identification of HIV-1 Transmission Networks from Nucleotide Sequences Sampled from Different Populations.

Author

Bbosa N, Ssemwanga D, and Kaleebu P

Subjects
Base Sequence, Cluster Analysis, Humans, Molecular Epidemiology, Phylogeny, HIV Infections epidemiology, HIV-1 genetics
Abstract

HIV-TRAnsmission Cluster Engine (HIV-TRACE) and Cluster Picker are some of the most widely used programs for identifying HIV-1 transmission networks from nucleotide sequences. However, choosing between these tools is subjective and often a matter of personal preference. Because these software use different algorithms to detect HIV-1 transmission networks, their optimal use is better suited with different sequence data sets and under different scenarios. The performance of these tools has previously been evaluated across a range of genetic distance thresholds without an assessment of the differences in the structure of networks identified. In this study, we tested both programs on the same HIV-1 pol sequence data set ( n  = 2,017) from three Ugandan populations to examine their performance across different risk groups and evaluate the structure of networks identified. HIV-TRACE that uses a single-linkage algorithm identified more nodes in the same networks that were connected by sparse links than Cluster Picker. This suggests that the choice of the program used for identifying networks should depend on the study aims, the characteristics of the population being investigated, dynamics of the epidemic, sampling design, and the nature of research questions being addressed for optimum results. HIV-TRACE could be more applicable with larger data sets where the aim is to identify larger clusters that represent distinct transmission chains and in more diverse populations where infection has occurred over a period of time. In contrast, Cluster Picker is applicable in situations where more closely connected clusters are expected in the studied populations.

Published
2020
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26. High Levels of Acquired HIV Drug Resistance Following Virological Nonsuppression in HIV-Infected Women from a High-Risk Cohort in Uganda.

Author

Segujja F, Omooja J, Lunkuse S, Nanyonjo M, Nabirye SE, Nassolo F, Bugembe DL, Bbosa N, Kateete DP, Ssenyonga W, Mayanja Y, Nsubuga RN, Seeley J, Kaleebu P, and Ssemwanga D

Subjects
Drug Resistance, Viral genetics, Female, Genotype, Humans, Mutation, Treatment Failure, Uganda epidemiology, Viral Load, Anti-HIV Agents pharmacology, Anti-HIV Agents therapeutic use, HIV Infections drug therapy, HIV Infections epidemiology, HIV-1 genetics
Abstract

HIV drug resistance (HIVDR) is of increasing health concern, especially among key populations. We investigated the prevalence of virological suppression (VS), prevalence and correlates of HIVDR in HIV-infected women, enrolled in a high-risk cohort. We enrolled 267 women initiated on first-line antiretroviral therapy (ART) between 2015 and 2018. Participants' plasma samples were analyzed for HIV RNA viral load (VL) and genotypic resistance testing was performed on those with VL nonsuppression (defined as VL ≥1,000 copies/mL). We used the Stanford HIVDR database-algorithm to assess HIVDR mutations and logistic regression to assess risk factors for VL nonsuppression and HIVDR. We observed an overall VS prevalence of 76.0% (203/267) and detected respective acquired drug resistance prevalence to non-nucleoside reverse transcriptase inhibitors (NNRTIs) and nucleoside reverse transcriptase inhibitors (NRTIs) of 81.3% [confidence interval (CI) 67.4-91.1] and 45.8% (CI 31.4-60.8) among the 48 successfully genotyped VL nonsuppressors. NNRTI mutations were observed in 81.3% (39/48) of the genotyped participants and 45.8% (22/48) had both NRTI and NNRTI mutations. The mutation K103N was detected in 62.5% (30/48) of participants, 41.7% (20/48) had M184V/I, 14.6% had K65R, and 12.5% (6/48) had thymidine analog mutations (TAMs). None of the analyzed potential risk factors, including age and duration on ART, was significantly correlated with VL nonsuppression or HIVDR. Although high levels of NNRTI mutations support the transition to dolutegravir, the presence of NRTI mutations, especially TAMs, may compromise dolutegravir-based regimens or other second-line ART options. The moderate VS prevalence and high HIVDR prevalence therefore call for timely ART switching and intensive adherence counseling.

Published
2020
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