Clinical epidemiology and high genetic diversity amongst Cryptococcus spp. isolates infecting people living with HIV in Kinshasa, Democratic Republic of Congo

Bive Zono Bive; Rosalie Sacheli; Hippolyte Situakibanza Nani-Tuma; Pius Kabututu Zakayi; Alex Ka; Marcel Mbula Mambimbi; Gaultier Muendele; Raphael Boreux; Nicole Landu; Celestin Nzanzu Mudogo; Pierre-Robert M’Buze; Michel Moutschen; Wieland Meyer; Georges Mvumbi Lelo; Marie-Pierre Hayette

doi:10.1371/journal.pone.0267842

. 2022 May 19;17(5):e0267842. doi: 10.1371/journal.pone.0267842

Clinical epidemiology and high genetic diversity amongst Cryptococcus spp. isolates infecting people living with HIV in Kinshasa, Democratic Republic of Congo

Bive Zono Bive ^1,^2,^*, Rosalie Sacheli ^2,^3,^#, Hippolyte Situakibanza Nani-Tuma ^4,^‡, Pius Kabututu Zakayi ^1,^‡, Alex Ka ^5,^‡, Marcel Mbula Mambimbi ^4,^‡, Gaultier Muendele ^6,^‡, Raphael Boreux ^2,^‡, Nicole Landu ^7,^‡, Celestin Nzanzu Mudogo ^1,^‡, Pierre-Robert M’Buze ^8,^‡, Michel Moutschen ^9,^‡, Wieland Meyer ^5,^10,^‡, Georges Mvumbi Lelo ^1,^#, Marie-Pierre Hayette ^2,^3,^#

Editor: Adriana Calderaro¹¹

¹Faculty of Medicine, Department of Basic Sciences, Molecular Biology Service, University of Kinshasa, Kinshasa, The Democratic Republic of Congo

²Centre for Interdisciplinary Research on Medicines, University of Liege, Liege, Belgium

³National Reference Centre for Mycosis, University Hospital Centre of Liege, Liege, Belgium

⁴Faculty of Medicine, Department of Internal Medicine/Department of Tropical Medicine, Infectious Diseases Service, University of Kinshasa, Kinshasa, The Democratic Republic of Congo

⁵Molecular Mycology Research Laboratory, Faculty of Medicine and Health, Centre for Infectious Diseases and Microbiology, Sydney Medical School, Westmead Clinical School, Marie Bashir Institute for Infectious Diseases and Biosecurity, University of Sydney, Westmead Hospital-Research and Education Network, Westmead Institute for Medical Research, Sydney, Australia

⁶Internal Medicine Department, Advanced HIV Disease Management Unit, Centre Hospitalier Mère et Enfant de NGABA, Kinshasa, The Democratic Republic of Congo

⁷Internal Medicine Department, Advanced HIV Disease Management Unit, Centre Médical et Evangélique Révérend LUYINDU, Kinshasa, The Democratic Republic of Congo

⁸Internal Medicine Department, Advanced HIV Disease Management Unit, Centre Hospitalier Roi Baudouin 1er, Kinshasa, The Democratic Republic of Congo

⁹Department of Infectious Diseases and General Internal Medicine, University Hospital Centre of Liege, Liege, Belgium

¹⁰Curtin Medical School, Curtin University, Perth, Australia

¹¹Universita degli Studi di Parma, ITALY

Competing Interests: The authors have declared that no competing interests exist.

‡ These authors also contributed equally to this work.

^✉

* E-mail: bive.zono@unikin.ac.cd

Contributed equally.

Roles

Bive Zono Bive: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – original draft

Rosalie Sacheli: Formal analysis, Writing – review & editing

Hippolyte Situakibanza Nani-Tuma: Data curation, Supervision, Validation, Writing – review & editing

Pius Kabututu Zakayi: Data curation, Validation, Writing – review & editing

Alex Ka: Formal analysis, Software, Writing – review & editing

Marcel Mbula Mambimbi: Validation, Writing – review & editing

Gaultier Muendele: Resources, Writing – review & editing

Raphael Boreux: Formal analysis, Validation, Writing – review & editing

Nicole Landu: Resources, Writing – review & editing

Celestin Nzanzu Mudogo: Validation, Writing – review & editing

Pierre-Robert M’Buze: Resources, Writing – review & editing

Michel Moutschen: Validation, Writing – review & editing

Wieland Meyer: Formal analysis, Validation, Writing – review & editing

Georges Mvumbi Lelo: Funding acquisition, Supervision, Validation, Writing – review & editing

Marie-Pierre Hayette: Funding acquisition, Supervision, Validation, Writing – review & editing

Adriana Calderaro: Editor

PMCID: PMC9119562 PMID: 35587939

Abstract

Neuromeningeal cryptococcosis (NMC) is a life-threatening opportunistic infection in advanced HIV disease patients (AHDP). It is caused by Cryptococcus spp. complexes and mainly occurs in sub-Saharan Africa. In this study, we performed molecular characterization and antifungal susceptibility profiling of Cryptococcus isolates from AHDP in Kinshasa (DRC). Additionally, we investigated a possible association between NMC severity factors and the Cryptococcus neoformans (Cn) multilocus sequence typing (MLST) profiles. We characterized the isolates using PCR serotyping, MALDI-TOF MS, internal transcribed spacer (ITS) sequencing, and MLST. Susceptibility testing for the major antifungal drugs was performed according to the EUCAST guidelines. Parameters associated with NMC severity, such as hypoglycorrhachia (< 50 mg/dL), increased cerebral spinal fluid opening pressure (> 30 cm H₂O), and poor therapeutic outcome were compared with the Cn MLST sequences type (ST). Twenty-three out of 29 Cryptococcus isolates were identified as serotype A using PCR serotyping (79.3%; 95% IC: 65.5–93.1), while six (20.7%; 95% IC: 6.9–34.5) were not serotypable. The 29 isolates were identified by ITS sequencing as follows: Cryptococcus neoformans (23/29, 79.3%), Cutaneotrichosporon curvatus (previously called Cryptococcus curvatus) (5/29, 17.2%), and Papiliotrema laurentii (Cryptococcus laurentii) (1/29, 3.5%). Using the ISHAM MLST scheme, all Cn isolates were identified as molecular type VNI. These comprised seven different STs: ST93 (n = 15), ST5 (n = 2), ST53 (n = 1), ST31 (n = 1), ST4 (n = 1), ST69 (n = 1), and one novel ST that has not yet been reported from other parts of the world and was subsequently assigned as ST659 (n = 2). Of the included strains, only Papiliotrema laurentii was resistant to amphoterin B (1/29, 3.5%), 6.8% (2/29) were resistant to 5-flucytosine (the single Papiliotrema laurentii strain and one Cryptococcus neoformans isolate), and 13.8% (4/29) to fluconazole, including two of five (40%) Cutaneotrichosporon curvatus and two of 23 (8.7%) C. neoformans strains. We found a significative association between poor therapeutic outcome and a non-ST93 sequence type of causative strains (these concerned the less common sequence types: ST53, ST31, ST5, ST4, ST659, and ST69) (87.5% versus 40%, p = 0.02). Molecular analysis of Cryptococcus spp. isolates showed a wide species diversity and genetic heterogenicity of Cn within the VNI molecular type. Furthermore, it is worrying that among included strains we found resistances to several of the commonly used antifungals.

Introduction

Among opportunistic infections encountered during HIV/AIDS, neuromeningeal cryptococcosis (NMC) causes 15% of deaths. Seventy-five per cent of deaths occur in sub-Saharan Africa, where the Democratic Republic of Congo (DRC) is also located. In this region, the annual mortality from this invasive fungal infection is estimated at 135,900 per year, making it one of the leading causes of death from infectious diseases [1].

The DRC is the second-largest African country with an area of 2,345,409 km² and a population of approximately 95,326,000. More than 60% of the population lives in rural areas. Its borders of about 9,165 kilometres are shared with nine neighbouring countries, namely: the Republic of Congo, Central African Republic, South Sudan, Uganda, Rwanda, Burundi, Tanzania, Zambia, and Angola [2].

The 2021 report of the United Nations Programme on AIDS (UNAIDS) in DRC estimated that 510,000 people were living with HIV (PLHIV) in 2020. Of these, approximately 25% (130,000) were not on antiretroviral treatment (ART) and therefore at risk of all the recognised complications of advanced HIV disease (AHD), such as cryptococcosis. In this HIV population at high risk of cryptococcosis, we include PLHIV in treatment failure and those on ART but lost to follow-up. Thus, in Kinshasa, the prevalence of NMC has previously been estimated at 8.8%, with a mortality rate of one in three patients [3,4].

The Cryptococcus neoformans/C. gattii species complexes (Cn/Cg) are the main etiological agents of cryptococcosis. Based on epidemiological, pathobiology, geographical distribution, ecological niches, clinical presentation, therapeutic, and genetic differences [5], seven distinct haploid species and four hybrid species are distinguished [6]. Apart from the Cn/Cg species complexes, others species previously described as non-neoformans/gattii Cryptococcus are also (rarely) associated with cryptococcal infection; these are now classified as members of other genera in the Tremellomycetes subphylum. This is the case for Papiliotrema laurentii (previously named Cryptococcus laurentii), Cutaneotrichosporon curvatus (Cryptococcus curvatus), Naganishia albidus (Cryptococcus albidus), Naganishia diffluens (Cryptococcus diffluens) [7–9].

Infections caused by specific Cryptococcus spp., such as C. gattii sensu lato (s.l), require more intensive therapy than those caused by C. neoformans s.l. Furthermore, several of the non-Cryptococcus species that cause cryptococcosis (according to the old taxonomy) exhibit intrinsic resistance to some of the commonly used antifungal agents [10–12]. Hence, surveillance data on circulating Cryptococcus spp. molecular types and their antifungal susceptibility profiles could inform the establishment of local therapeutic guidelines.

As the association of Cn/Cg molecular type with the antifungal susceptibility profile has previously been established, it is also opportune to verify the association of molecular types, or even MLST sequence types (ST), with clinical presentation. Therefore, we investigated whether NMC severity factors could be linked with the sequence type of the causative strain [13].

Here we describe the NMC clinical epidemiology amongst advanced HIV disease patients (AHDP), the molecular characterization of strains, as well as the antifungal susceptibility of Cryptococcus spp. isolates. In addition, the association between NMC severity factors and Cryptococcus neoformans MLST sequence types was statistically analysed.

The findings of this study will allow programme decision-makers and health care providers to consider the local specificities of this deadly mycosis, in terms of epidemiological magnitude, circulating species, clinical severity and antifungal susceptibility. In addition, they will serve as a reference in molecular epidemiology and antifungal susceptibility for future surveillance studies, which should be performed regularly.

Materials and methods

Study design, patients and samples

A cross-sectional study was conducted in three Kinshasa public hospitals supported by Doctors without Borders-Belgium (MSF), from 1 February 2019 to 29 February 2020. Thus, 278 patients were included and among them, NMC was diagnosed based on the cryptococcal antigens (CrAg) detection and/or the presence of yeasts cells detected by India ink staining and/or by culture.

Biological analysis

The CrAg detection was carried out in the cerebral spinal fluid (CSF) of each included patient, using the CrAg LFA IMMY test (Immuno-mycologic, Norman, OK, USA). Direct staining with India ink in the CSF was also carried out, and the CSF was cultured on Sabouraud Dextrose Agar-Chloramphenicol medium (SDA-C, bioMérieux, France) at 30°C for 48 to 72h. The qualitative Pandy test was performed for detecting elevated protein levels in CSF, as previously described [14].

Identification by MALDI-TOF MS

Fungal identification was performed using MALDI-TOF MS on a Bruker Microflex instrument (Bruker Daltonics GmbH, Bremen, Germany). From the culture on SDA-C, a partial extraction procedure was performed by adding 1 μL of 70% formic acid to the sample on a target plate (MSP 96 BC ground steel target; Bruker Daltonics). Then, 1 μL of saturated cyano-4-hydroxycinnamic acid solution (HCCA matrix; Bruker Daltonics) was added. Each microorganism tested was spotted twice on the same target plate. Measurements were performed with the Flex control V3.4 software (Bruker Daltonics), using default settings and BD 8326 (version V. 9.0) as a reference library [15]. The following criterion was used for reliable identification of fungal species: an MS Score ≥ 1.5 and the 3 first results identical and consistent with the appearance of the colonies on agar (with some adjustments according to the scores profile for difficult cases)

Molecular analysis

DNA extraction. Genomic DNA was extracted from the fresh 24-hour cultures using the NucleoSpin blood quick pure kit (Macherey-Nagel, Düren, Germany). Two preliminary steps were added to the manufacturer’s protocol, namely bead-beating and thermal shock. In a 2mL tube containing 0.5mm glass beads (Roche Diagnostics GmbH, Penzberg, Germany), colonies were mixed with 350 μL lysis buffer (Promega Corporation, Madison, Wisconsin, USA). The mixture was vortexed five times at 6000 revolutions per minute for 40 seconds (bead-beating). Between each pass, the tube was cooled between -20°C and 1°C for 30 seconds in a Nalgene microtube cooler container (Dutscher, Bernolsheim, France) for a thermal shock.

Serotyping PCR. A classical serotyping PCR designed for Cn/Cg species complexes was performed according to the protocol described by Ito-Kuwa et al. [16].

ITS sequencing. The ITS2 region of the rRNA gene cluster was amplified using the ITS86 forward primer 5′GTGAATCATCGAATCTTTGAA 3′ and ITS4 reverse primer 5′TCCTCCGCTTATTGATATGC 3′ [17]. The amplified products were purified using the clean Seq Agencourt kit (Beckman Coulter Life Sciences, Indianapolis, Indiana, USA). Sequencing was done on an ABI 3500/3500XL automate (Applied Biosystems, Thermo Fisher Scientific, Waltham, Massachusetts, USA). Bidirectional sequence data were generated after purification using the BigDye terminator sequencing kit (Applied Biosystems). Sequences generated by the software ABI Sequence Scanner V.1.0 (Applied Biosystems) were then compared to the CBS database by using The BioloMICS database software (https://wi.knaw.nl/page/Pairwise_alignment), which comprises several databases including Genbank. Only results that repeated the same identification at least three times and had a similarity score greater than 95% were considered valid.

Multilocus sequence typing. Multilocus sequence typing (MLST) was performed using the International Society of Human and Animal Mycology (ISHAM) consensus scheme for the Cn/Cg species complexes; including six unlinked housekeeping loci (GPD1, LAC1, URA5, SOD1, CAP59, and PLB1) and the non-coding region IGS1 [18]. After DNA extraction, samples were sequenced using Illumina HiSeq (Illumina, San Diego, California, USA) as previously described [19], and the raw contigs sequences were paired, removed of duplicate reads, and trimmed using Geneious Prime 64_2021_1 (https://www.geneious.com). Then, the MLST loci were extracted by mapping to the reference sequences of each MLST locus from the online ISHAM MLST fungal database (https://mlst.mycologylab.org/) and each allele type (AT) was assigned using the same online database. The ATs combination defines the sequences type (ST) which in most cases corresponds to the molecular type of isolate.

Minimum expansion trees were generated using the geoBURST algorithm to determine the STs clonal complexes (CCs) representing linked and closely related clusters (https://online.phyloviz.net). Apart from STs of C. neoformans s.l and C. gattii s.l reference strains (WM) included in the analysis, we also inserted MLST sequences of the single C. neoformans strain (ZS) previously isolated from a Congolese infected patient and recorded in the International Fungal MLST Database (DRC, previously called Zaire).

Antifungal susceptibility testing

Antifungal drugs’ minimal inhibitory concentrations (MICs) were determined according to EUCAST guidelines (European Committee on Antimicrobial Susceptibility Testing) E.Def 7.3.1 [20]. Suspensions of 0.5 McFarland standard were prepared and diluted 1:10 with sterile distilled water (Sensititre demineralized water, Thermo Fisher Scientific). Plates contained the following concentration ranges: 0.008–8 mg/L for amphotericin B, and 0.06–64 mg/L for both 5-flucytosine and fluconazole. The reading of the MIC50 value (drug concentration resulting in 50% inhibition of microorganisms) for 5-flucytosine and fluconazole, and MIC90 for amphotericin B, was done according to EUCAST recommendations, both visually and using an automated reading at 405 nm with a Multiscan FC spectrophotometer (Thermo Fisher Scientific). Candida parapsilosis ATCC 22019 and Candida krusei ATCC 6258 were used as the quality control strains for the tests. The interpretation criteria for amphotericin B were those defined in the antifungals EUCAST breakpoint tables’ version 10.0: susceptible, ≤ 1 mg/L; resistant, > 1 mg/L. Since no EUCAST breakpoints were defined for fluconazole and 5-flucytosine, interpretation criteria were based on the epidemiological cut-off values for in vitro susceptibility testing provided by the Clinical and Laboratory Standards Institute (CLSI) for C. neoformans (AFLP1/VNI) as follows: for both fluconazole and 5-flucytosine, sensitive, ≤ 8 mg/L; resistant, > 8 mg/L [21].

Statistical analysis

Statistical analyses were carried out using R-cmdr version 2.6–1 (R Foundation for Statistical Computing, Vienna, Austria). Missing data were considered completely random and the available data were analysed. The continuous variables were summarised as mean ± standard deviation and compared using Student’s t-test. The proportions and their respective 95% confidence intervals were calculated for the categorical data. The main outcome variable was the NMC diagnosis. This variable was compared to other variables of the same category using Pearson’s chi-square test or Fisher’s exact test if the expected values were less than five. Raised CSF opening pressure (> 30 cm H₂O), hypoglycorrhachia (< 50 mg/dL) and poor therapeutic outcome were considered in performing the association analysis between the NMC severity factors and the identified ST-MLST profile. Thus, two isolates categories were formed according to the STs-MLST profile: the main ST isolated (ST93) on the one hand, and the other STs on the other hand. All tests were two-tailed and a p < 0.05 was considered statistically significant.

Comparative data between PLHIV with Cryptococcus neoformans versus Cutaneotrichosporon curvatus and Papiliotrema laurentii meningitis are presented in another published paper.

Ethical considerations

This work was carried out in strict compliance with ethical rules, with the approval of the Ethics Committee of the Public Health School of the Faculty of Medicine of the University of Kinshasa under the approval number ESP/CE/071/2019. All patients included in this study were informed of the risks associated with the study and gave their written informed consent to participate. Anonymity was guaranteed and the data collected was kept and handled by the research team alone.

Results

Among 278 advanced HIV disease patients (AHDP) who were hospitalized with suspected cryptococcosis and underwent lumbar puncture during the study period, 66 (23.7%, 95% CI: 18.7–28.8) had NMC. The NMC prevalence was similar in males (24.8%, 95% CI: 14.9–34.7, 25/101 included males) and in females (23.2%, 95% CI: 15.7–30.7, 41/177 included females).

Patients’ characteristics

The demographic and clinical characteristics of the patients are presented in Table 1. The mean age of the included patients was 42.2±12.1 years old. Most of them concerned females (63.7%), married or cohabitating (49.3%), and had a secondary education level (55.8%). Regarding the HIV clinical stage of patients before NMC diagnosis, NMC tended to develop during stage IV (95.5%, p = 0.0008). AHDP with NMC had four times higher probability to present headaches (OR 3.8; IC 95%: 1.7–8.8; p = 0.0001), three times higher probability to present convulsions (OR 2.7; IC 95%: 1.01–7.1, p = 0.02); and six times higher probability to present visual disturbances than no NMC patients (OR 5.6; IC 95%: 1.04–37.5, p = 0.02). The vast majority of NMC patients had clear CSF (93.9%) and a significantly raised CSF opening pressure (65.4%, p<0.0001). Furthermore, the poor therapeutic outcome was not significantly different between NMC and non-NMC patients (37.5 versus 35.4%, respectively).

Table 1. Demographic and clinical characteristics of patients.

Characteristics¹	Overall data (%)²	NMC		p-value	Crude OR (95% CI)
Characteristics¹	Overall data (%)²	No (%)	Yes (%)	p-value	Crude OR (95% CI)
Demographic characteristics
Mean age ± SD (years) (n = 278)	42.2 ± 12.1	42.8 ± 12.0	40.2 ± 12.4	0.1
Female sex (n = 278)	177 (63.67)	136 (64.2)	42 (62.1)	0.7
Marital status (n = 278)				0.6
Single	89 (32.01)	71 (33.5)	18 (27.3)
Married/cohabitating	137 (49.28)	102 (48.1)	35 (53.0)
Divorced/widower	52 (18.7)	39 (18.4)	13 (19.7)
Education level attained (n = 278)				0.6
None/primary	69 (24.82)	52 (24.5)	17 (25.8)
Secondary	155 (55.76)	121 (57.1)	34 (51.5)
Higher education/university	54 (19.42)	39 (18.4)	15 (22.7)
Clinical characteristics
HIV clinical stage (n = 242)				0.0008
Stage I	1 (0.41)	1 (0.5)	0 (0.0)
Stage II	2 (0.83)	2 (0.9)	0 (0.0)
Stage III	51 (21.07)	48 (22.6)	3 (4.5)
Stage IV	224 (92.6)	161 (75.9)	63 (95.5)
Headaches (n = 269)	174 (64.68)	120 (58.8)	54 (84.4)	0.0001	3.8 (1.7–8.8)
Fever (°C) (n = 269)	179 (66.54)	141 (68.8)	38 (59.4)	0.1
Weight loss (n = 269)	144 (53.53)	107 (52.2)	37 (57.8)	0.4
Consciousness disorder (n = 269)	100 (37.17)	82 (40.0)	18 (28.1)	0.08
Memory impairment (n = 268)	74 (27.61)	59 (28.9)	15 (23.4)	0.3
Neck stiffness (n = 269)	49 (18.22)	33 (15.6)	16 (24.2)	0.1
Vomiting (n = 269)	54 (20.07)	42 (20.5)	12 (18.8)	0.7
Convulsions (n = 269)	23 (8.6)	13 (6.3)	10 (15.6)	0.02	2.7 (1.01–7.1)
Vertigo (n = 269)	32 (11.9)	23 (11.2)	9 (14.1)	0.5
Brudzinski sign (n = 269)	15 (5.58)	8 (3.8)	7 (10.6)	0.05
Physical asthenia (n = 269)	30 (11.1)	23 (11.2)	7 (10.9)	0.9
Kernig sign (n = 269)	14 (5.2)	9 (4.2)	5 (7.6)	0.3
Visual disturbances (n = 269)	8 (2.97)	3 (1.5)	5 (7.8)	0.02	5.6 (1.04–37.5)
Functional Impairment (n = 269)	17 (6.32)	12 (5.9)	5 (7.8)	0.5
Clear CSF appearance (n = 265)	249 (93.9)	200 (94.3)	62 (93.9)	1
Raised CSF opening pressure (cm H₂O) (n = 92)	65 (70.6)	7 (10.6)	17 (65.4)	<0.0001
Antiretroviral therapy (ART) (n = 278)	204 (73.4)	153 (72.2)	51 (77.3)	0.4
Poor outcome³ (n = 217)	78 (35.9)	57 (35.4)	21 (37.5)	0.7

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¹according to available data.

²column per cent calculated for each group.

³Death, status quo, discharge against medical advice, or transfer due to complications.

Biological analysis

Routine diagnostic analysis of NMC

Out of 66 NMC samples confirmed, 63 (95.5%, 95% CI: 89.4–100) had detectable cryptococcal antigen, only 29 (43.3%, 95% CI: 31.8–56.1) had yeasts presence after India ink staining, and the repeated culture was positive only in 43.3% of the cases (29/66, 95% CI: 31.8–56.1). All three CrAg negative samples were recovered from positive cultures.

MALDI-TOF MS, ITS sequencing, and PCR serotyping characterization

Of the 29 positive cultures, MALDI-TOF MS identified 23 as C. neoformans (79.3%), four as Cutaneotrichosporon curvatus (previously called Cryptococcus curvatus) (13.8%), and two (6.9%) could not be identified. While only 23 isolates were identified as serotype A using PCR serotyping (79.3%), six isolates were not serotypable (20.7%). All isolates were identified by ITS sequencing as follows: C. neoformans 79.3% (23/29), Cutaneotrichosporon curvatus 17.2% (5/29), and Papiliotrema laurentii (Cryptococcus laurentii) 3.5% (1/29). The results of the MALDI-TOF MS, ITS sequencing, and PCR serotyping characterization are summarized in Table 2. Additionally, MALDI-TOF MS detailed findings are presented in Table 3.

Table 2. MALDI-TOF MS, ITS sequencing, and multiplex PCR serotyping characterization.

Analysis	n = 29 (%)
MALDI-TOF MS
Cryptococcus neoformans	23 (79.3)
Cutaneotrichosporon curvatus	4 (13.8)
Not identified	2 (6.9)
ITS sequencing
Cryptococcus neoformans	23 (79.3)
Cutaneotrichosporon curvatus	5 (17.2)
Papiliotrema laurentii	1 (3.5)
Serotyping PCR
Serotype A	23 (79.3)
No identifiable	6 (20.7)

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Table 3. MALDI-TOF MS detailed findings.

Strain ID	Best match organism (score value)	Second-best match organism (score value)	Third-best match organism (score value)	NCBI identifier of the best match organism
BZ-3NGA	Cryptococcus neoformans P152 CBS (2.0)	Cryptococcus neoformans CBS 8710T CBS (1.81)	Cryptococcus neoformans CBS 996 CBS (1.81)	178876
BZ-6NGA	Cryptococcus neoformans var. grubii CBS 10512 CBS (1.59)	Cryptococcus neoformans var. grubii CBS 8710 CBS (1.46)	Cryptococcus neoformans var. grubii P152 CBS (1.40)	178876
BZ-9NGA	Cryptococcus neoformans var. grubii P152 CBS (1.85)	Cryptococcus neoformans var. grubii CBS 996 CBS (1.67)	Cryptococcus neoformans CBS 8710 CBS (1.58)	178876
BZ-13NGA	Cryptococcus neoformans var. grubii CBS 996 CBS (2.01)	Cryptococcus neoformans var. grubii P152 CBS (1.97)	Cryptococcus neoformans ICB 178 CBS (1.74)	178876
BZ-24NGA	Cryptococcus neoformans var. grubii CBS 996 CBS (1.74)	Cryptococcus neoformans var. grubii P152 CBS (1.60)	Cryptococcus neoformans CBS 10512 CBS (1.49)	178876
BZ-42NGA¹	Brevundimonas nosdae DSM 14572T HAM (1.45)	Strepmyces griseus B261 UFL (1.16)	Millerozyma farinozo DSM 2226T DSM (1.16)	33069
BZ-44NGA	Cryptococcus neoformans P152 CBS (1.75)	Cryptococcus neoformans CBS 8710T CBS (1.53)	Cryptococcus neoformans ICB 165 CBS (1.45)	178876
BZ-46NGA	Cryptococcus neoformans var. grubii CBS 10512 CBS (1.53)	Cryptococcus neoformans var. grubii CBS 996 CBS (1.51)	Cryptococcus neoformans var. grubii ICB 165 CBS (1.50)	178876
BZ-73NGA	Cryptococcus neoformans var. grubii P152 CBS (1.74)	Cryptococcus neoformans var. grubii ICB 178 CBS (1.69)	Cryptococcus neoformans var. grubii CBS 996 CBS (1.55)	178876
BZ-94NGA²	Pseudomonas viridiflava DSM 11124T HAM (1.26)	Cellulocimicrobium cellulans B480 UFL (1.19)	Pseudomonas pertucinogena LMG 1874T HAM (1.19)	33069
BZ-97NGA	Cryptococcus neoformans P152 CBS (1.99)	Cryptococcus neoformans 29 PSB (1.86)	Cryptococcus neoformans ICB165 CBS (1.82)	178876
BZ-103NGA	Cryptococcus neoformans P152 CBS (1.89)	Cryptococcus neoformans ICB 165 CBS (1.63)	Cryptococcus neoformans CBS 996 CBS (1.55)	178876
BZ-105NGA	Cryptococcus curvatus CBS 570T CBS (1.51)	Clostridium Haemolyticum 1069 ATCC 9650T BOG (1.46)	Rhizobium radiobacter DSM 30147T HAM (1.28)	57679
BZ-110NGA	Cryptococcus neoformans P152 CBS (1.83)	Cryptococcus neoformans CBS 996 CBS (1.69)	Cryptococcus neoformans CBS 8710T CBS (1.62)	178876
BZ-124NGA	Cryptococcus neoformans P152 CBS (1.84)	Cryptococcus neoformans CBS 8710T CBS (1.81)	Cryptococcus neoformans CBS 10085 CBS (1.79)	178876
BZ-4RB	Cryptococcus neoformans P152 CBS (2.0)	Cryptococcus neoformans CBS 996 CBS (1.82)	Cryptococcus neoformans CBS 8710T CBS (1.75)	178876
BZ-12RB	Cryptococcus neoformans P152 CBS (1.80)	Cryptococcus neoformans CBS 8710T CBS (1.69)	Cryptococcus neoformans CBS 10512 CBS (1.62)	178876
BZ-22RB	Cryptococcus neoformans P152 CBS (1.78)	Cryptococcus neoformans CBS 10512 CBS (1.75)	Cryptococcus neoformans CBS 10085 CBS (1.63)	178876
BZ-27RB	Cryptococcus neoformans var. grubii CBS 10512 CBS (1.41)	Cryptococcus neoformans var. grubii ICB 178 CBS (1.35)	Cryptococcus neoformans var. grubii P152 CBS (1.32)	178876
BZ-28RB	Cryptococcus neoformans var. grubii P152 CBS (1.81)	Cryptococcus neoformans var. grubii CBS 10512 CBS (1.72)	Cryptococcus neoformans var. grubii CBS 996 CBS (1.60)	178876
BZ-55RB	Cryptococcus neoformans P152 CBS (2.02)	Cryptococcus neoformans CBS 8710T CBS (1.81)	Cryptococcus neoformans CBS 996 CBS (1.73)	178876
BZ-68RB	Cryptococcus neoformans CBS 996 CBS (1.69)	Cryptococcus neoformans P152 CBS (1.68)	Cryptococcus neoformans CBS 8710T CBS (1.57)	178876
BZ-78RB	Cryptococcus neoformans P152 CBS (1.85)	Cryptococcus neoformans CBS 8710T CBS (1.61)	Cryptococcus neoformans CBS 996 CBS (1.45)	178876
BZ-88RB	Cryptococcus neoformans var. grubii P152 CBS (1.60)	Cryptococcus neoformans var. grubii P10512 CBS (1.50)	Cryptococcus neoformans var. grubii ICB 165 (1.41)	178876
BZ-91RB	Cryptococcus neoformans CBS 8710T CBS (1.75)	Cryptococcus neoformans P152 CBS (1.55)	Cryptococcus neoformans 29 PSB (1.46)	178876
BZ-14LU	Cryptococcus neoformans var. grubii P152 CBS (1.54)	Cryptococcus neoformans var. grubii CBS 10085 CBS (1.54)	Cryptococcus neoformans var. grubii CBS 10512 CBS (1.48)	178876
BZ-23LU	Cryptococcus curvatus CBS 570T CBS (1.70)	Cellulocimicrobium cellulans B480 UFL (1.36)	Cryptococcus neoformans ICB175 SDA CBS (1.28)	57679
BZ-29LU	Cryptococcus curvatus CBS 570T CBS (1.52)	Pseudomonas rhizosphaerae LMG 21640T HAM (1.39)	Agromyces cerinus ssp cerinus HKI 11525_DSM 8595T HKJ (1.28)	57679
BZ-33LU	Cryptococcus curvatus CBS 570T CBS (1.91)	Cryptococcus curvatus CBS 5163 CBS (1.46)	Pseudomonas graminis DSM 11363T HAM (1.36)	57679

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¹Strain later identified as Cryptococcus laurentii.

²Strain later identified as Cryptococcus curvatus.

MLST results

Apart from the six strains identified as Cutaneotrichosporon curvatus (five) and Papiliotrema laurentii (one), the remaining 23 Cryptococcus neoformans isolates belong to the molecular type VNI. MLST analysis identified seven different STs: ST93 (15 isolates, 65.2%), ST5 (two isolates, 8.6%), ST53 (one isolate, 4.3%), ST31 (one isolate, 4.3%), ST4 (one isolate, 4.3%), ST69 (one isolate, 4.3%), and one novel ST that was not yet reported in the online fungal MLST database and was later assigned as ST659 (two isolates, 8.6%).

The application of the geoBURST algorithm to Cryptococcus neoformans (ST) isolates in the study identified two clonal complexes (CCs): CC1 (ST31 and ST93, as well as ST32 which was isolated 30 years earlier in the DRC) and CC2 (ST5 and ST53); and three singletons: ST659, ST4 and ST69 (Fig 1).

Fig 1 — Clonal colours indicate the source of the isolates, distinguishing the study isolates, former DRC isolate, and the reference isolates. Blue circle shows CC1 and the red circle shows CC2.

Antifungal susceptibility test

Overall, only the single strain of Papiliotrema laurentii was found to be resistant to amphotericin B (1/29, 3.4%). Concerning 5-flucytosine, two of 29 strains exhibited high MICs (6.9%), including the Papiliotrma laurentii strain and a strain of Cryptococcus neoformans. About 4/29 (13.8%) strains were categorized as resistant to fluconazole, including two Cutaneotrichosporon curvatus and two Cryptococcus neoformans strains. Considering only Cryptococcus neoformans strains, fluconazole resistance (MIC value > 8 mg/L) was identified in 8.6% (2/23) of the cases.

NMC severity factors and MLST ST of Cryptococcus neoformans isolates

Among NMC severity factors previously described and those considered in the present study, only the poor therapeutic outcome was associated with infections due to the less common MLST STs isolates (ST5, ST659, ST53, ST31, ST4, and ST69) versus the main ST (ST93) (87.5% vs. 40%, respectively; p = 0.02). Table 4 summarizes the NMC severity factors compared to the MLST ST of C. neoformans isolates.

Table 4. NMC severity factors compared to the MLST STs of C. neoformans isolates.

Variable	Cryptococcus neoformans ST identified		p
Variable	Main study ST¹ n³ (%)⁴	Less common STs² n³ (%)⁴	p
Glycorhachia (mg/dl) (n = 10)			1
Low (≤ 50)	7 (87.5)	2 (100)
High (≥ 60)	1 (12.5)	0
Opening pressure (cm H₂O) (n = 8)			0.4
Moderately high (<30)	2 (40)	0
Very high (≥30)	3 (60)	3 (100)
Therapeutic outcome (n = 23)			0.02
Good⁵	9 (60)	1 (12.5)
Poor⁶	6 (40)	7 (87.5)

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¹ST93.

²STs5, 659, 53, 31, 4, 69.

³With available data.

⁴Percentage of columns calculated for each group.

⁵Recovery and discharge from hospital.

⁶Death, status quo, discharge against medical advice, or transfer due to complications.

Discussion

We described the clinical epidemiology of neuromeningeal cryptococcosis (NMC) in advanced HIV disease patients (AHIVP) admitted to the tertiary facilities, as well as the results of antifungal susceptibility testing and molecular characterization of Cryptococcus spp. isolates. In addition, we investigated the association between NMC severity factors and Cryptococcus neoformans MLST sequence types.

Neuromeningeal cryptococcosis (NMC) prevalence in AHDP was estimated at 23.7% (95% CI: 18.7–28.8). This prevalence is much higher than that reported in France [22], in other African countries [23–26], and previously in the DRC [4]. However; it is approximately similar to that described in Cameroon [27] and slightly lower than that reported in Kenya [28]. This could be explained by (a) differences in HIV infection prevalence in various countries and regions, (b) the HIV management and opportunistic infections prevention policy applied in each country, (c) characteristics of the patient population included in each study, (d) the sensitivity and specificity of diagnostic tests used, and (e) the species/molecular types/sequences type circulating in each region. Female patients aged 42.19 ± 12.13 years old, married/cohabitating and with secondary education level were (non-significantly) most affected. This trend is consistent with the demographic profile of PLHIV in the DRC. McClelland E. et al. found that virulent C. neoformans phenotypes from females had longer doubling times and released more capsular glucuronoxylomannan (GXM) in the 17-β estradiol presence. Additionally, macrophages from women phagocytized more C. neoformans than those from men. Males hence had a higher fungal load than females and their macrophages were more likely to be destroyed by C. neoformans [29]. This protective trend in women was not significantly noted in the current study. Headache, convulsions and visual disturbances were significantly associated with NMC. These data are largely consistent with the literature [30]. Visual disturbances are known to be associated with NMC in 18% of cases following raised intracranial pressure, which is slightly more than the proportion reported in this study (7.7%) [31]. Among the clinical symptoms and signs (headache, depressed sensorium, papilledema, and raised CSF opening pressure) and radiological findings (flattening of the posterior sclera, increased CSF in the subarachnoid space around the optic nerve, optic nerve tortuosity and empty parietal saddle), only headaches and raised CSF opening pressure were found in the present study [31]. NMC was associated with higher raised CSF opening pressure, CD4 count < 100 cells/mm³ and HIV-infection stage IV. One of the most critical outcome determinants in PLHIV with NMC is the raised opening CSF pressure which is generally correlated with high CSF fungal load, co-morbidities and increased risk of death [32]. In agreement with the data described by Bicanic et al, 63% of patients in the present study had a very high opening CSF pressure [33].

Cryptococcal antigen (CrAg) detection was the main diagnostic tool for NMC in the present study (95% positivity rate out of 66 confirmed samples). Compared to other studies, culture positivity rate, and Cryptococcus India ink staining identification were very low [34]. For Kabanda T. et al, CrAg detection has shown more sensitivity in clinical situations (100%) than culture (95.7%) and India ink staining (93.6%) [35]. The low positivity rate of direct microscopy and culture found in this study could be caused by the suboptimal storage conditions of samples before analyses and/or probable low CSF fungal load in certain samples [36].

ITS sequencing yielded an identification for all included strains (29/29). Described as the main responsible species for cryptococcosis diseases in PLHIV [37], C. neoformans was identified in 73.3% of all isolates. The remaining cases were identified as Cutaneotrichosporon curvatus (17.2%) and Papiliotrema laurentii (3.5%). Initially considered saprophytic and non-pathogenic to humans, these last two non-Cryptococcus species previously described as non-neoformans/gattii Cryptococcus species, are becoming increasingly common in clinical infections [7,10,38]. Although the clinical presentation of C. neoformans NMC is more severe than in non-Cryptococcus NMC, some Cutaneotrichosporon curvatus and Papiliotrema. laurentii strains exhibited resistance to common antifungal agents, such as 5-flucytosine and amphotericin B. In addition, these species are more difficult to identify by routine laboratory methods than C. neoformans [7]. In this study, out of six non-Cryptococcus species, only four were correctly identified by MALDI-TOF MS compared to the identification obtained using ITS sequencing. The lack of certain species reference spectra in the database provided by the mass spectrometry manufacturer (reference library MBT Filamentous Fungi Library 3.0, Bruker Daltonics) for identification, and genome differences between Cryptococcus and non-Cryptococcus species which would result in huge differences in proteomic profiles between these species, could explain these results. Inconclusive MALDI TOF MS identifications were therefore associated with poor MALDI TOF MS scores. Serotype A isolates were previously reported to be the most commonly isolated serotypes in environmental and clinical settings [39], both worldwide and in DRC, a trend also confirmed in the current study.

The MLST analysis of C. neoformans isolates revealed a large heterogeneity of STs within the single molecular type found in the present study (VNI), involving seven distinct STs. In line with our results, Cryptococcus neoformans ST93 is the most isolated in various countries (China, India, Indonesia, South Africa, Thailand, Brazil, Uganda, and Colombia), both in clinical and environmental settings [40–45]. This ST has been associated with high mortality in Uganda [43]. However, the STs distribution amongst Cryptococcus neoformans VNI population in Ivory Coast is dominated by ST5, while ST93 is only found in 1.3% of cases [46]. In Nigeria, ST32 has been repeatedly reported [47]. Therefore, each region should identify the genetic profile of the strains circulating and determine epidemiological, clinical and therapeutic implications, in order to adapt regional management guidelines.

In this study, ST93 was associated with better treatment outcomes than the less common STs. This opens up the debate on the relative virulence of each ST compared to the others. Although most of the STs had already been isolated in the DRC neighbouring countries and recorded in the International Fungal MLST Database, namely ST4 in Uganda and Tanzania, ST5, ST31, ST69, and ST93 in Uganda only. One ST (ST53) had previously been isolated only in Thailand and in no other country worldwide (https://mlst.mycologylab.org/) [45]. In addition, one isolate had an ST identified for the first time in the present study, subsequently assigned as ST659. STs analysis based on the geoBURST algorithm revealed a strong link between ST32 (a Congolese strain isolate more than 30 years earlier) and the CC1 identified in this study. This could suggest a descending evolution between these STs.

Overall, one isolate of Papiliotrema. laurentii was resistant to amphotericin B and 5-flucytosine, and four strains had high MICs values for fluconazole, including two Cryptococcus neoformans and two Cutaneotrichosporon curvatus. These results may partly explain the high mortality rates previously described in Congolese studies [4,48]. With triple antifungal therapy routinely administered to all NMC patients, the antifungal resistances described here did not significantly affect patients’ outcomes. Nevertheless, large-scale evaluations should be conducted and treatment regimens updates should be made if necessary.

As described by Trilles et al. regarding the low antifungal susceptibility of VGI isolates compared to the other molecular types tested [49], the less common STs isolates identified in the present study were associated with poor therapeutic outcomes.

Conclusions

Using a combination of diagnostic tests, we report a higher epidemiological burden of NMC among symptomatic AHDP in DRC than previously described. The following fungal species were identified in CSF samples: Cryptococcus neoformans, Cutaneotrichosporon. curvatus and Papiliotrema. laurentii. MLST analysis demonstrated a variety of sequence types within the only molecular type found (VNI), including one major ST (ST93) and six less common STs (ST5, ST659, ST53, ST31, ST4, and ST69). We report a fluconazole resistance rate of 13.8% (4/29), in addition to strains with sporadic resistance to amphotericin B and 5-flucytosine; this requires careful monitoring. Additionally, we find that non-ST93 sequence types were associated with poor therapeutic outcomes. Larger studies are required to consolidate the findings from this study.

Supporting information

S1 File. CryptoDepositSheetNov2020_Bive (1).

(XLSX)

Click here for additional data file.^{(115KB, xlsx)}

S2 File. Cutaneotrichospora curvatus-Papiliotrema laurentii ITS-2.

(DOCX)

Click here for additional data file.^{(22.8KB, docx)}

S3 File. ITS 2 Cryptococcus spp. sequences_PLOS ONE.

(FASTA)

Click here for additional data file.^{(9.5KB, fasta)}

Acknowledgments

The authors sincerely thank Dr Klaas Dewaela for the editing and proofreading of the English language.

Data Availability

Clinical and genomic data from Cryptococcus neoformans infections were submitted and integrated into the International Multilocus Sequence Typing (MLST) Data base (https://mlst.mycologylab.org/). The novel Sequence Type (ST) isolated for the first time in our study (not yet reported from other parts of the world) was assigned as ST659 and can be found as such in the database. We attach the submission sheet for easy access. Concerning Cutaneotrichospora curvatus/Papiliotrema laurentii infection data, the genomic (and clinical) information of the ITS-2 sequences as submitted to GenBank have also been uploaded as supporting information file. In addition, all ITS-2 sequences extracted in the study have been provided as supplementary information.

Funding Statement

This study was funded by ARES-Belgium (Académie de Recherche et d’Enseignement Supérieur), through an institutional support programme to the University of Kinshasa (under the grant number: COOP-CONV-18-004, AI ARES UNIKIN). The study’s funder had no role in study design, data collection, data analysis, data interpretation, or writing of the report.

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18 Feb 2022

PONE-D-21-40474Clinical epidemiology and high genetic diversity amongst Cryptococcus spp. isolates infecting people living with HIV in Kinshasa, Democratic Republic of CongoPLOS ONE

Dear Dr. ZONO,

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Although well conducted, the study requires further experiments; in particular the Authors should evaluate the susceptibility of strains to antifungal agents. Furthermore, the manuscript requires a strong revisions concerning English Language and an update of the bibliography.

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( It is noteworthy that comparative data between PLHIV with Cryptococcus neoformans versus Cryptococcus curvatus/ C. laurentii meningitis are presented in another published paper (https://doi.org/10.1186/s12879-021-06849-3). Although carried out in the same study population by the same research team, the data are presented separately without any interference)

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Reviewers' comments:

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Comments to the Author

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The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: No

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This is a well written manuscript that addresses a pathogen that continues to cause high morbidity and mortality among PLHIV with advanced disease. What i see missing are implication of this work programmatically. Nonetheless, this is a well written review

Reviewer #2: The manuscript by Zono Bive et al. deals with the genetic diversity of Cryptococcus strains isolated in the Democratic Republic of Congo. They use ITS sequencing, MALDI-TOF and MLST. The article is well conducted despite a limited number of strains. The structure of the article is logical and well built. However, the English language could be proofread. An evaluation of the susceptibility of the tested strains is also missing.

Concerning the remarks, they are essentially general:

- The new nomenclature of Cryptococcus laurentii and Cryptococcus curvatus (i.e. Cutaneotrichospora and Papiliotrema) should be used (Liu 2015, Sujita 2017)

- The epidemiological data used (Park 2009) are obsolete. Those published by Rajasingham in 2017 (ref. 1) should be cited

- p 6. The 194 correct 55.8

- Tables should be reformatted according to journal standards

- Regarding Table 2, the MALDI-TOF scores should be specified to know the reliability of the identifications. This could explain the wrong identification of Papiliotrema.Percentages are not really useful in this table.

- Figure 1 is not appropriate. It is necessary to generate a Minimum Spanning Tree and to place the STs in a global environment allowing to determine CCs.

- There is a serious lack of data on the susceptibility of strains to major antifungal drugs. This would strengthen this study and strongly support the Discussion.

- In the Discussion section (p10. L 266-268): the references are too old (ref. 18) and the choice of countries is not relevant. There are very good studies on cryptococcosis in Africa and they deserve to be added to your Discussion.For example, there are studies in Cameroon, Côte d'Ivoire or Kenya (Loyse 2019, Kassi 2019, Gitonga 2019).

- P10. L 268-272: ST can also explain your hypothesis.

- p11. L 300-302: C. laurentii has long been known to cause infections in humans (Kamalam 1976, Lynch 1981).

- P 11. L 313-314: Here again, it is imperative to compare the data with those published in other African countries (e.g. ST 5 and 93 are present in Ivory Coast).

-P 11. L317-322: please cite references

**********

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2022 May 19;17(5):e0267842. doi: 10.1371/journal.pone.0267842.r002

Author response to Decision Letter 0

19 Mar 2022

Dear Editor,

First of all, I would like to thank you and the reviewers of this manuscript through you, who have clearly and significantly contributed to its improvement.

As mentioned in the manuscript, this work is from the cross-sectional study conducted by our team in tertiary hospitals in Kinshasa (DRC). The initial results led to the elaboration of a comparative study of the baseline clinical and biological profiles of patients with Cryptococcus neoformans versus non-neoforman/non-gattii (non-Cryptococcus) meningitis, exclusively the 29 patients who had a positive culture. This article has been peer-reviewed and published online: https://doi.org/10.1186/s12879-021-06849-3.

In the current manuscript, we discuss the general results of this epidemiological investigation (cross-sectional study), based on the clinical profile of the included subjects, routine biological data, molecular identification (PCR serotyping, MALDI TOF MS, ITS sequencing, MLST characterization) and antifungal susceptibility of Cryptococcus spp. isolates. All patients’ data are included here. These are therefore not the same data profiles.

In the following lines, you will find our detailed answers.

Point-by-point response

I. Academic editor’ comments

- Abstract: Could do with a line on implications for programs.

R/ Lines on the implications for programs have been added in the introductory part of the manuscript. Lines 129-133

- Introduction: Given the size of DRC, could do with some further data that show the variability by geography

R/ The additional data on geographical variability in the DRC has been added in the text. Lines 93-97

- Results: Would have been important to include some cascade data that starts with all who were hospitalized, how many had suspected cryptococcal disease and how many had a lumbar puncture.

R/ Following the suggestion, we specified here that all included patients (278) were hospitalized and had a lumbar puncture for investigation. Lines 246-250

- A couple of grammatical areas to look at. Line 189 and 190: “men patients” and “women patients” seems odd. Would rather use, “men” and “women”.

R/ Changes made in the manuscript. Lines 248-250

- Line 195- “stage before NMC diagnosis, NMC tends to develop during HIV-infection stage IV (95.5%, p = 0.0008)”. Would use “tended” instead of “tends”.

R/ Changes made in the text. Line 255

- Line 196: Use four times “higher” probability.

R/ Changes made in the manuscript. Lines 256-259

- In general some sentences are written in present tense while others are presented in past tense.

R/ Uniformity has been revised in the text.

Discussion:

- Line 263: “We described the clinical epidemiology of PLHIV” could be re-written to specify that the authors described the clinical epidemiology of PLHIV with advanced HIV admitted to a tertiary facility and not all PLHIV.

R/ Changes made in the text. Lines 329-331

II. Reviewer 1’ comments

- What I see missing are implication of this work programmatically.

R/ The programmatic implications of this work have been incorporated into the manuscript. Lines 129-133

III. Reviewer 2’ comments

3.1. Main comment

- The English language could be proofread.

R/ Based on the suggestion, the manuscript was reviewed and edited by a native English-speaking.

- An evaluation of the susceptibility of the tested strains is also missing.

R/ The susceptibility of the strains to antifungal agents was tested and included in the manuscript.

3.2. General remarks

- The new nomenclature of Cryptococcus laurentii and Cryptococcus curvatus (i.e. Cutaneotrichospora and Papiliotrema) should be used (Liu 2015, Sujita 2017).

R/ The new nomenclature has been updated in the manuscript and the corresponding references were also updated.

- The epidemiological data used (Park 2009) are obsolete. Those published by Rajasingham in 2017 (ref. 1) should be cited.

R/ Changes made in the manuscript. Lines 88-92

- p 6. The 194 correct 55.8

R/ Changes made in the text. Line 254

- Tables should be reformatted according to journal standards

R/ improvements have been made in the concerned files

- Regarding Table 2, the MALDI-TOF scores should be specified to know the reliability of the identifications. This could explain the wrong identification of Papiliotrema.

R/ A supplementary table containing details of the MALDI-TOF MS findings including scores has been added.

- Figure 1 is not appropriate. It is necessary to generate a Minimum Spanning Tree and to place the STs in a global environment allowing to determine CCs

R/ Minimum Spanning Trees using the geoBURST algorithm were generated and the corresponding figure has been added.

- There is a serious lack of data on the susceptibility of strains to major antifungal drugs. This would strengthen this study and strongly support the Discussion.

R/ The susceptibility of strains to major antifungal drugs was tested and discussed in the manuscript.

- In the Discussion section (p10. L 266-268): the references are too old (ref. 18) and the choice of countries is not relevant. There are very good studies on cryptococcosis in Africa and they deserve to be added to your Discussion. For example, there are studies in Cameroon, Côte d'Ivoire or Kenya (Loyse 2019, Kassi 2019, Gitonga 2019).

R/ Update done in the manuscript. For this discussion part, the first intention was to compare the hospital prevalence of NMC in HIV populations in different regions. Articles with this baseline data have been completed, including the great suggested articles that have been extensively used in the rest of the discussion. Lines 334-337

- P10. L 268-272: ST can also explain your hypothesis.

R/ Suggestion taken into account in the manuscript. Lines 338-342

- P11. L 300-302: C. laurentii has long been known to cause infections in humans (Kamalam 1976, Lynch 1981).

R/ Correction taken into account in the manuscript. Lines 374-378

- P 11. L 313-314: Here again, it is imperative to compare the data with those published in other African countries (e.g. ST 5 and 93 are present in Ivory Coast).

R/ References from studies conducted in African regions on MLST characterisation of Cryptococcus neoformans/Cryptococcus gattii strains were sought and considered. Lines 393-407

- P 11. L317-322: please cite references

R/ It should be noted here that all STs mentioned in this section were obtained from the online database: international fungal MLST database: https://mlst.mycologylab.org/. Details have been provided in the manuscript and corresponding references have been cited.

Attachment

Submitted filename: Response to Reviewers.docx

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PLoS One. doi: 10.1371/journal.pone.0267842.r003

Decision Letter 1

Adriana Calderaro

18 Apr 2022

Clinical epidemiology and high genetic diversity amongst Cryptococcus spp. isolates infecting people living with HIV in Kinshasa, Democratic Republic of Congo

PONE-D-21-40474R1

Dear Dr. ZONO,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Adriana Calderaro

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

**********

6. Review Comments to the Author

Reviewer #1: Excellent responses to the review comments. The paper reads well with a good description of the context, the impact of cryptococcosis and implications for clinical care clearly laid out.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

PLoS One. doi: 10.1371/journal.pone.0267842.r004

Acceptance letter

Adriana Calderaro

12 May 2022

PONE-D-21-40474R1

Clinical epidemiology and high genetic diversity amongst Cryptococcus spp. isolates infecting people living with HIV in Kinshasa, Democratic Republic of Congo

Dear Dr. Bive:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

MD, PhD, Associate Professor Adriana Calderaro

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 File. CryptoDepositSheetNov2020_Bive (1).

(XLSX)

Click here for additional data file.^{(115KB, xlsx)}

S2 File. Cutaneotrichospora curvatus-Papiliotrema laurentii ITS-2.

(DOCX)

Click here for additional data file.^{(22.8KB, docx)}

S3 File. ITS 2 Cryptococcus spp. sequences_PLOS ONE.

(FASTA)

Click here for additional data file.^{(9.5KB, fasta)}

Attachment

Submitted filename: PONE-D-2140474 Reviewer comments.docx

Click here for additional data file.^{(13.7KB, docx)}

Attachment

Submitted filename: PONE-D-21-40474.pdf

Click here for additional data file.^{(790.5KB, pdf)}

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(30KB, docx)}

Data Availability Statement

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PERMALINK

Clinical epidemiology and high genetic diversity amongst Cryptococcus spp. isolates infecting people living with HIV in Kinshasa, Democratic Republic of Congo

Bive Zono Bive

Rosalie Sacheli

Hippolyte Situakibanza Nani-Tuma

Pius Kabututu Zakayi

Alex Ka

Marcel Mbula Mambimbi

Gaultier Muendele

Raphael Boreux

Nicole Landu

Celestin Nzanzu Mudogo

Pierre-Robert M’Buze

Michel Moutschen

Wieland Meyer

Georges Mvumbi Lelo

Marie-Pierre Hayette

Roles

Abstract

Introduction

Materials and methods

Study design, patients and samples

Biological analysis

Identification by MALDI-TOF MS

Molecular analysis

Antifungal susceptibility testing

Statistical analysis

Ethical considerations

Results

Patients’ characteristics

Table 1. Demographic and clinical characteristics of patients.

Biological analysis

Routine diagnostic analysis of NMC

MALDI-TOF MS, ITS sequencing, and PCR serotyping characterization

Table 2. MALDI-TOF MS, ITS sequencing, and multiplex PCR serotyping characterization.

Table 3. MALDI-TOF MS detailed findings.

MLST results

Fig 1. Minimum Spanning Tree showing the distribution of the 23 Cryptococcus neoformans (STs, VNI) isolates from advanced HIV disease patients using geoBURST analysis.

Antifungal susceptibility test

NMC severity factors and MLST ST of Cryptococcus neoformans isolates

Table 4. NMC severity factors compared to the MLST STs of C. neoformans isolates.

Discussion

Conclusions

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Adriana Calderaro

Roles

Transfer Alert

Author response to Decision Letter 0

Decision Letter 1

Adriana Calderaro

Roles

Acceptance letter

Adriana Calderaro

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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