Gut Microbiota Alteration Is Characterized by a Proteobacteria and Fusobacteria Bloom in Kwashiorkor and a Bacteroidetes Paucity

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OPEN Gut Microbiota Alteration is Characterized by a Proteobacteria and Fusobacteria Bloom in Received: 14 January 2019 Accepted: 14 May 2019 Kwashiorkor and a Bacteroidetes Published: xx xx xxxx Paucity in Marasmus Thi-Phuong-Thao Pham1, Maryam Tidjani Alou1,2, Dipankar Bachar1, Anthony Levasseur1, Souleymane Brah3, Daouda Alhousseini3, Cheikh Sokhna 4, Aldiouma Diallo4, Frank Wieringa 5, Matthieu Million1 & Didier Raoult1

Kwashiorkor and marasmus are considered to be two diferent clinical diseases resulting from severe malnutrition, but this distinction has been questioned. In a previous study comparing children with kwashiorkor and healthy children from Niger and Senegal, we found a dramatic gut microbiota alteration with a predominant depletion of anaerobes and enrichment in Proteobacteria and Fusobacteria in kwashiorkor. However, it remained unknown whether this association was related to malnutrition or was a specifc feature of kwashiorkor. In this continuation study, we added 7 new marasmus subjects and 71,162 new colonies from the same countries. Our results showed that, compared to marasmus, the kwashiorkor gut microbiota was characterized by an increased proportion of Proteobacteria (culturomics, Marasmus 5.0%, Kwashiorkor 16.7%, p < 0.0001; metagenomics, Marasmus 14.7%, Kwashiorkor 22.0%, p = 0.001), but there was a decreased proportion of Bacteroidetes in marasmus (culturomics, Marasmus 0.8%, Kwashiorkor 6.5%, p = 0.001; metagenomics, Marasmus 5.4%, Kwashiorkor 7.0%, p = 0.03). Fusobacterium was more frequently cultured from kwashiorkor. All detected potential pathogenic species were enriched in the kwashiorkor gut microbiota. These results provide a biological basis to support the usage of an antibiotic therapy more efective in suppressing the overgrowth of bacterial communities resistant to penicillin, combined with antioxidants and probiotics for nutritional recovery therapies, particularly for kwashiorkor.

Child malnutrition is a global health challenge and despite recent progress, is responsible for 45% of under-fve mortality1. In addition to adequate water, protein and energy intake, the discovery of the critical importance of micronutrients and the generalization and application of World Health Organization (WHO) standardized protocols, including ready-to-use therapeutic foods and antibiotics, have largely contributed to recent achievements. However, new therapeutic options are warranted because 10–30% of children with complicated severe acute malnutrition (SAM) do not respond to optimal WHO-compliant management in randomized controlled studies2,3. Among the various diseases associated with malnutrition, there is controversy over the defnition of marasmus and kwashiorkor as two distinct diseases. Indeed, the WHO criteria no longer distinguish between the treatment ofered for the two diseases and considers that although the diferent forms of malnutrition can be distinguished by the presence of oedema, the treatment does not address the pathophysiological diferences. According to the WHO, SAM is defned in children under fve years of age by a weight-for-height z-score (WHZ) below −3

1Aix-Marseille Univ, IRD, APHM, MEPHI, IHU-Méditerranée Infection, Marseille, France. 2Institut de cancérologie Gustave Roussy Cancer Campus, Villejuif, France. 3Service de Médecine Interne et Générale, Hôpital de Niamey, Niamey, Niger. 4Unité de Recherche sur les Maladies Infectieuses et Tropicales Emergentes IRD 198, Centre National de la Recherche Scientifque 7278, Aix-Marseille Université, Dakar, Senegal. 5Institut de Recherche pour le Développement (IRD) - IRD/UM/SupAgro, Montpellier, France. Correspondence and requests for materials should be addressed to M.M. (email: [email protected]) or D.R. (email: [email protected])

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standard deviations (SD) and/or bilateral oedema and/or, only in children over six months of age, a mid-upper arm circumference (MUAC) < 115 mm4. While an epidemiological approach is efective for identifying public health issues to guide international policy decisions and humanitarian fnancial investments, it may have limited ability to resolve diferent disease entities. A clinical approach may more efciently detect diferent syndromes associated with diferent mechanisms, prognosis and most importantly require diferent treatment for optimum case management. Kwashiorkor is such a clinical disease characterised by oedema and frst named by Cicely D. Williams in 19335. Studies have consistently reported distinctive clinical features supporting the notion that kwashiorkor is diferent from marasmus (Supplementary Table S1A), but children can have both conditions (marasmic-kwashiorkor). Kwashiorkor appears with an acute onset four to twelve months afer the onset of defective feeding, while the patient appears to develop normally. Irritability then begins, accompanied by attacks of diarrhoea, swelling of the hands and feet, ‘moon face’, depigmented hair and skin changes (depigmentation, thickened black and crumpled patches). Mucous membranes are frequently infamed and ulcerated, and infection is probably ubiquitous. Tere is usually extreme fatty infltration of the liver. Meanwhile marasmus can occur at any age and is characterized by a loss of muscle mass and subcutaneous fat5. In a quantitative meta-analysis of published comparative studies of marasmus and kwashiorkor6–18 (Fig. 1, Supplementary Table S1B, Supplementary Fig. S1), we found that kwashiorkor was consistently associated with a decrease in zinc, glutathione, total protein, albumin and transferrin, but ferritin consistently tended to increase (Supplementary Fig. 1B). While a descending gradient from controls to patients with marasmus and kwashiorkor could be observed for minerals, antioxidants and protein levels; the massive increase in liver fat was only observed with kwashiorkor, confrmed by the meta-analysis. Tis suggests a specifc metabolic process is at play in kwashiorkor, but not in marasmus, in the context of a severely restricted and inadequate diet. Based on recent fndings19, gut dysbiosis may be a discriminant feature between kwashiorkor and marasmus. Gut microbiota alteration was found to be instrumental in weight loss and malnutrition20. An impover- ished microbiota and a predominant loss of strict anaerobes associated with an increased gut redox potential was observed in children with kwashiorkor21. Strikingly, non-alcoholic steatohepatitis (NASH) in adults is another disease associated with both an unhealthy diet and fatty liver. In this disease, fatty liver has been linked to gut dysbiosis; specifcally, a Proteobacteria, Enterobacteriaceae, and particularly Escherichia enrichment associated with endogenous ethanol22. Similar mechanisms may be at play in kwashiorkor pathogenesis and could impact the choice of antibiotics used in kwashiorkor management. In this study, we investigated whether alterations to the gut microbiota in healthy children, and in children diagnosed with marasmus or kwashiorkor exhibited a gradient that paralleled the decreasing mineral, antioxidant, and protein levels observed in our meta-analysis. We also expected that gut dysbiosis in kwashiorkor could be a potential key factor in fatty liver pathogenesis, as previously reported for NASH. As no children with marasmus were included in our previous study23, it was not clear whether this association was related to malnutrition or if this was a specifc feature of kwashiorkor. In this culturomics study, we added 7 new marasmus subjects corresponding to 71,162 new colonies to clarify the gut microbiota variations between marasmus and kwashiorkor. Metagenomics was used as a confrmatory technique. Results Gut microbial diversity revealed a decreasing gradient from controls to children with marasmus and kwashiorkor. A total of 22 samples from fve healthy controls, seven marasmus and ten kwashiorkor cases were analysed by using 18 culture conditions. Culturomics yielded 276,070 colonies, allowing us to identify 281 bacterial species in healthy controls (CTL), 224 species in marasmus (MRS) and 335 species in kwashiorkor (KW). Te β-diversity dramatically decreased from controls (ratio of the number of unique species on total species in each group (U/T ratio) – see methods) at 65.7%, to marasmus at 55.8% and kwashiorkor at 45.0%. Te diferences between the three groups were signifcant (uncorrected chi-square test: CTL-MRS, p = 0.02; CTL-KW, p = 2.5*10−7; MRS-KW, p = 0.013; Table 1). In addition, we identifed 44 species that were previously unknown to be present in the human gut, including 20 putative new species (Supplementary Table S3), 12 species previously unknown to be present in humans and only two species that were previously known to be present in humans but were not gut-associated (Table 1). A decreasing gradient was observed from CTL to MRS and KW, signifcant for β-diversity (U/T ratio - CTL > MRS > KW; Cochran-Armitage trend test, p = 0.0002; Fig. 2A), but not signifcant for the hitherto unknown bacteria’s diversity (Supplementary Fig. S2). With metagenomics, a total of 1,842,831 reads, 1,960,894 reads and 2,933,416 reads were generated in control, marasmus and kwashiorkor samples, respectively. Tere was no signifcant diference in the number of raw reads between the 3 groups (Mean ± SD, CTL 368,566 ± 148,491; MRS 280,128 ± 28,855; KW 293,342 ± 145,673, one-way ANOVA test, p = 0.44; Supplementary Table S2). Te number of reads not assigned to a prokaryotic species was 101,848 ± 82,799 for controls, 24,134 ± 20,848 for marasmus and 14,954 ± 12,661 for kwashiorkor (Supplementary Table S2). Te diference was signifcant (one-way ANOVA test, p = 0.003) with a linear trend (CTL > MRS > KW, post-test for linear trend, p = 0.001). In addition, a clear gradient (CTL > MRS > KW) was observed for the mean Shannon index (Mean ± SD): CTL 3.65 ± 0.67, MRS 3.48 ± 0.48 and KW 2.97 ± 0.7 (Fig. 2D); however, no signifcant diferences were found between the three groups and the test for trend was not signifcant (Table 2). Tis may be due to the limited sample size compared with the culturomics analysis.

The gradient was more pronounced for anaerobic than for aerotolerant diversity. By culturomics, anaerobic β-diversity assessed by the U/T ratio was 67.85% for CTL, 49.31% for MRS and 38.73% for KW, with a significant difference between groups (uncorrected chi-square test: CTL-MRS, p = 0.012; CTL-KW, p = 0.00001). In parallel, for aerotolerant species, the U/T ratio was 64.5% for CTL, 59% for MRS and 48% for KW, with a signifcant diference between groups (uncorrected chi-square test: CTL-KW, p = 0.001;

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Figure 1. Meta-analysis results of studies analysing the diferences in intermediary metabolism patterns between KW and MRS (only signifcant results are shown).

MRS-KW, p = 0.041) (Table 1). Interestingly, we also found a signifcant decreasing trend in anaerobic diversity (Cochran-Armitage trend test, p = 0.0002), which was greater and more signifcant than the decrease in aerotolerant diversity (Cochran-Armitage trend test, p = 0.001) (Fig. 2B,C). Te data obtained from our metagenomic results showed the same decreasing gradient (Fig. 2E,F) in anaerobic diversity (mean Shannon index: CTL 3.1 ± 0.8, MRS 2.8 ± 0.6 and KW 2.57 ± 1.2). For aerotolerant diversity, no diference was noted (CTL 2.7 ± 0.8, MRS 2.7 ± 0.36 and KW 2.5 ± 0.5). None of the diferences were statisti- cally signifcant (Table 2).

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Mean ± SD/Proportions Comparison (p-value) CTL (n = 5) MRS (n = 7) KW (n = 10) CTL-MRS CTL-KW MRS-KW CTL-MRS-KW β diversity (proportions) 185/281 125/224 151/335 All species (Ratio U/T) 0.02d 2.5*10−7d 0.013d 0.0002c 65.71% 55.80% 45% 76/112 36/73 43/111 Anaerobic species 0.012d 0.00001d 0.15d 0.0002c 67.85% 49.31% 38.73% 109/169 89/151 108/224 Aerotolerant species 0.3d 0.001d 0.041d 0.001c 64.50% 59% 48% Global diversity (mean ± SD) Number of phyla 3.8 ± 0.4 3.4 ± 0.8 4.2 ± 0.6 0.14a 0.2a 0.03b No gradient Number of genera 34 ± 12 22 ± 5 36 ± 7 0.06b 0.67a 0.003b No gradient Total number of species 92 ± 20 67 ± 14 89 ± 22 0.07a 0.82a 0.03b No gradient Number of putative new species 5 ± 3 3 ± 2 2 ± 1 0.22a 0.009b 0.28a 0.06e Number of non-human species 2.8 ± 0.4 1.6 ± 1.7 1.5 ± 1.2 0.23a 0.02a 0.92a 0.28e Phyla distribution (proportions of each phylum in a group) 42/281 12/224 47/335 Actinobacteria 0.001d 0.74d 0.001d No gradient 15% 5.40% 13.70% 21/281 2/224 21/335 Bacteroidetes 0.0004d 0.55d 0.001d No gradient 7.50% 0.80% 6.50% 25/281 11/224 56/335 Proteobacteria 0.082d 0.004d 2.5*10−5d No gradient 8.90% 5% 16.70% 192/281 198/224 208/335 Firmicutes <10−7d 0.1d <10−7d No gradient 68.30% 88.40% 61.70% 0/281 1/224 3/335 Fusobacteria 0.88f 0.32f 0.53d No gradient 0% 0.40% 0.80%

Table 1. Descriptive results of culturomics in healthy controls and patients with marasmus and kwashiorkor. aTwo-tailed Student’s t-test; bMann-Whitney test; cCochran-Armitage trend test; duncorrected chi-square test; elinear regression test; fFisher’s exact test. U/T: unique/total species ratio. New species: unknown species identifed for the frst time. Non-human species: previously known species identifed in human for the frst time.

Globally, the decrease in diversity was more pronounced and signifcant for anaerobic than for aerotolerant species in both the culturomics and metagenomics results (Supplementary Fig. S3A,B).

Principal coordinate analysis. We have performed a PCoA analysis to test whether the microbiota profle analysed by culturomics or metagenomics discriminated individuals based on their nutritional status, age, gender and geographical origin. Te results showed that only culturomics clearly distinguished between children with marasmus and kwashiorkor (Fig. 3A), regardless of their age, sex or geographical origin (Supplementary Fig. S4). On the other hand, the analysis of microbiota by metagenomics was not able to discriminate between the diferent groups regarding the detection or the relative abundance of each species (Fig. 3B,C). For other clinical variables, metagenomics yielded age and geographical origin clustering but not the nutritional status. Tis was found only by the detection of each species but not by relative abundance (Supplementary Fig. S4). By adding the controls in the analysis, we observed that in culturomics, the CTL formed a distinct group from MRS but was mixed with KW (Supplementary Fig. S5). Paucity of Bacteroidetes in marasmus samples and bloom of Proteobacteria and Fusobacteria in kwashiorkor samples. Te culturomics results for marasmus samples identifed a total of 5 bacterial phyla containing 224 species, including 198 Firmicutes (88.40%), 12 Actinobacteria (5.40%), 11 Proteobacteria (5%), 2 Bacteroidetes (0.80%) and only 1 Fusobacteria (0.40%). Focusing on Proteobacteria (CTL 8.90%, MRS 5% and KW 16.70%) and Bacteroidetes (CTL 7.50%, MRS 0.80% and KW 6.50%), we found a signifcant increase in Proteobacteria in kwashiorkor (uncorrected chi-square test: CTL-KW, p = 0.004; MRS-KW, p = 2.5*10−5) and a signifcant decrease in Bacteroidetes in marasmus (uncorrected chi-square test: CTL-MRS, p = 0.0004; MRS-KW, p = 0.001; Table 1). Strikingly, no Fusobacteria species were isolated from CTL, although one and three Fusobacteria species were isolated from MRS and KW, respectively. Te metagenomics results confrmed the changes in Proteobacteria and Bacteroidetes found by culturomics (Fig. 4). Tere was a signifcant increase in Proteobacteria in kwashiorkor samples (CTL 15%, MRS 14.77% and KW 22%; uncorrected chi-square test: CTL-KW, p = 0.004; MRS-KW, p = 0.001) and a signifcant decrease in Bacteroidetes in marasmus samples (CTL 9%, MRS 5.40% and KW 7%; uncorrected chi-square test: CTL-MRS, p = 0.0011; MRS-KW, p = 0.03; Table 2). Metagenomics also confrmed the increase in Fusobacteria species in kwashiorkor samples (CTL 1/486 versus KW 7/589, unilateral mid-P exact test: p = 0.034) but not in marasmus samples (Table 2).

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Figure 2. Analysis of gut microbial diversity revealed a decreasing gradient from controls to children with marasmus and kwashiorkor. Scatter plots visually display gradients across the 3 groups in descending order, from controls (CTL) to children with marasmus (MRS) to kwashiorkor (KW). (A–C) Culturomics: A/global β-diversity (U/T ratio, see methods), Cochran-Armitage trend test, p = 0.0002; B/anaerobic β-diversity, p = 0.0002; C/aerotolerant β-diversity, p = 0.001. (D–F) Metagenomics. Te decreasing gradient was signifcant for culturomics results (A–C) but not for metagenomics results (D–F).

Kwashiorkor microbial diversity demonstrated greater numbers of pathogenic species than marasmus microbial diversity. To more accurately characterize the species that were enriched or depleted in patients with kwashiorkor versus marasmus, we choose 30 bacterial species exhibiting the largest diferences in relative frequency (culturomics) and relative abundance (metagenomics) between these two groups. With culturomics, Fig. 5 show that there were 5 Proteobacteria species four of which were potential pathogenic and two aerobic pathogens enriched in kwashiorkor. All the potential pathogenic species were aerobic. Tere were no potential pathogenic species among the bacteria enriched in the marasmic children (One-Sample test for binomial proportion: p = 0.025 for the Proteobacteria proportion, p = 0.014 for the potential pathogenic species proportion). According to the metagenomics results, 18 species were enriched in kwashiorkor samples and 12 species in marasmus samples (Fig. 6). Tere were 11 Proteobacteria species three of which were potential pathogenic and one further aerobic pathogen enriched in kwashiorkor. Tere were no Proteobacteria species and no potential pathogenic bacteria enriched in marasmus (One-Sample test for binomial proportion: p = 0.00091 for Proteobacteria, p = 0.045 for pathogenic species). Accordingly, culturomics (6 species: Escherichia coli, Gemella haemolysans, Hafnia alvei, Klebsiella pneumoniae, Salmonella enterica, Streptococcus mitis) was better than metagenomics (4 species: Acinetobacter baumanii, Enterobacter cloacae, Hafnia alvei, Klebsiella oxytoca) for the detection of pathogenic bacterial enrichment in kwashiorkor. In marked contrast no potential pathogens were enriched by either technique from marasmus. Tus, both culturomics and metagenomics results confrmed higher numbers of Proteobacteria and potential pathogenic species, mainly belonging to the Gammaproteobacteria and Bacilli families, in the guts of children with kwashiorkor than in children with marasmus. In addition, a similar comparison at species level between marasmus and control groups (Supplementary Figs S6, S7) confrmed the proliferation of Proteobacteria and potential pathogenic species was only observed in children with kwashiorkor. Discussion Whether kwashiorkor and marasmus are two diferent diseases, or a continuum of a single entity has, in the past been subject to controversy. However, two separate diseases, like many other conditions, can occur simultane- ously in the same child. A mixed clinical presentation is then referred to as marasmic-kwashiorkor. In general, such children have a worse prognosis (23% mortality without antibiotics) than that of children with isolated marasmus (8% mortality) or isolated kwashiorkor (5% mortality)2. Despite the diferences in clinical features (Supplementary Table S1A), there is a marked overlap between the biological features of kwashiorkor and marasmic patients (Supplementary Table S1B), making it hard to pin-point specifc features that are unique to

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Mean ± SD/Proportions Comparison (p-value) CTL (n = 5) MRS (n = 7) KW (n = 10) CTL-MRS CTL-KW MRS-KW CTL-MRS-KW Global diversity (mean Shannon index by group) Global diversity 3.65 ± 0.67 3.48 ± 0.48 2.97 ± 0.7 0.63a 0.1a 0.12a 0.12e Anaerobic diversity 3.1 ± 0.8 2.8 ± 0.6 2.57 ± 1.2 0.49a 0.37a 0.61a 0.58e Aerotolerant diversity 2.7 ± 0.8 2.7 ± 0.36 2.5 ± 0.5 0.94a 0.57a 0.34a 0.67e Phylum distribution (proportions of each phylum in a group) 61/486 75/482 86/589 Actinobacteria 0.68d 0.37d 0.55d No gradient 12% 16% 14% 46/486 26/482 44/589 Bacteroidetes 0.0011d 0.24d 0.03d No gradient 9% 5.40% 7% 75/486 86/482 131/589 Proteobacteria 0.76d 0.004d 0.001d No gradient 15% 14.77% 22% 296/486 285/482 319/589 Firmicutes 9.6*10−5d 0.026d 0.075d No gradient 61% 49% 54% 1/486 2/482 7/589 Fusobacteria 0.67d 0.062d 0.097d No gradient 0.20% 0.34% 1.20% 0/486 1/482 0/589 Cyanobacteria 0.54f NA 0.49f No gradient 0% 0.17% 0% 1/486 1/482 1/589 Verrucomicrobia 0.89d 0.89d 0.99d No gradient 0.20% 0.17% 0.16% 0/486 0/482 1/589 Chlorofexi NA 0.36f 0.50f No gradient 0% 0% 0.16% 4/486 0/482 0/589 Euryarchaeota 0.085f 0.082f NA No gradient 0.80% 0% 0% 1/486 0/482 0/589 Lentisphaerae 0.91f 0.90f NA No gradient 0.20% 0% 0% 1/486 0/482 0/589 Tenericutes 0.91f 0.9f NA No gradient 0.20% 0% 0%

Table 2. Descriptive results of metagenomics in healthy controls and patients with marasmus and kwashiorkor. aTwo-tailed Student’s t-test; bMann-Whitney test; cCochran-Armitage trend test; duncorrected chi-square test; elinear regression test; fFisher’s exact test; NA: non-available.

kwashiorkor or marasmus. In addition, inconsistent results have led to confusion in diferentiating kwashiorkor and marasmus. Our quantitative meta-analysis of published comparative studies of marasmus and kwashiorkor (Fig. 1, Supplementary Table S1B, Supplementary Fig. S1) showed that, compared to marasmus, kwashiorkor was consistently associated with the depletion of several plasmatic components, including proteins (total protein, albumin, transferrin), antioxidant molecules (glutathione), and minerals (zinc). For the frst time, to our knowl- edge, these results reconcile two opposing visions of protein defciency and oxidative stress. Moreover, albumin and transferrin are major antioxidants in plasma24,25. Te depletion of other antioxidant molecules such as vita- min A and C in kwashiorkor was demonstrated as early as 195826. Although oedema is the main clinical feature of kwashiorkor, fatty liver is not included in the clinical defnition, nevertheless it is a ubiquitous feature26. Based on recent fndings in NASH, a candidate mechanism for alcoholic fatty liver is the gut microbiota alteration22. Overall, gut dysbiosis appears to be an instrumental factor in the hitherto unknown pathophysiological process resulting in both fatty liver and oedema in kwashiorkor. Tis prompted us to investigate the gut microbiota by both culturomics and metagenomics, which are the two major approaches for characterization of the human microbiota27 in both marasmus and kwashiorkor. In this study, we observed a depletion in global diversity due to anaerobic and aerotolerant diversity depletion in the gut microbiota of children with both kwashiorkor and marasmus. Te depletion was more profound in kwashiorkor than marasmus and afected anaerobic species to a great extent than aerobic species. It is notewor- thy that Methanobrevibacter smithii, the most oxygen-sensitive prokaryote of the human gut, was not detected in children with SAM23. Our results not only confrmed alteration of the gut microbiota, especially anaerobic diversity, in SAM children, as reported in previous studies21,23, but also showed a decreasing gradient of diversity (CTL > MRS > KW). Te unknown microbial abundance (unassigned, not yet cultivated) was 5 times higher in controls than in children with marasmus. Tis confrms the loss of diversity. Tis had already been found in our previous study comparing controls and children with kwashiorkor23. We have shown here a gradient that confrms a loss of diversity in malnutrition and that it is even more important in the case of kwashiorkor. Our fndings led us to propose a theory linking oxidative stress seen in children with kwashiorkor, and to a lesser extent in marasmus, to the depletion of anaerobic diversity in children with marasmus and kwashiorkor. These results were consistent with the lower levels of glutathione, minerals and protein observed in our meta-analysis. On the one hand the gut microbiome is a potential source of hepato-toxins, other noxious

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Figure 3. PCoA analysis of gut microbiota diversity among kwashiorkor and marasmus subjects. Te 3 charts are allowing us to visualize the proximities between kwashiorkor and marasmus subjects. For culturomics (A), there was a clear distance between 2 groups (KW and MRS) In metagenomics, analysis based on the relative abundance (B) and on the detection (C) of each species in each group showed that the clustering was not observed. Te colour coded as red for MRS group and blue for KW group.

Figure 4. Phylum distribution in culturomics and metagenomics. Culturomics and metagenomics results confrmed a signifcant increase in Proteobacteria in kwashiorkor (uncorrected chi-square test, CTL-KW, MRS-KW, p < 0.001) as well as a signifcant decrease in Bacteroidetes in marasmus (uncorrected chi-square test, CTL-MRS, MRS-KW, p < 0.0005). Strikingly, the increased Fusobacteria diversity in kwashiorkor observed by metagenomics was also confrmed in kwashiorkor, but not in marasmus, by culturomics (uncorrected chi- square test, CTL-KW, p = 0.004).

products and pro-oxidants. On the other hand, a lack of dietary anti-oxidants would make intestinal anaerobic species vulnerable to oxidative damage.

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Figure 5. Increased frequency of species in kwashiorkor and marasmus samples identifed by the culturomics approach. Each bar represents the relative diference for each species, with red bars representing an increased frequency in patients with kwashiorkor and green bars representing an increased frequency in patients with marasmus. Te grey bars represent oxygen tolerant species and the black bars represent obligate anaerobes. Proteobacteria (in orange) and potential pathogenic bacteria (p) were enriched in kwashiorkor but not in marasmus. **p-value ranging from 0.001–0.01; *p-value ranging from 0.01–0.05 (two-tailed chi-square test, Fisher’s exact test).

In our study, children were included and assigned to the marasmus and kwashiorkor groups mainly based on the clinical assessment made during the hospital admission (Supplementary Table S4). Indeed, we deliberately choose to assess the pathophysiological mechanisms of these clinically diferent diseases and not malnutrition severity (moderate versus severe). However, we observed a decreasing gradient from controls to marasmus and kwashiorkor in our meta-analysis for minerals, antioxidants and protein levels (Fig. 1, Supplementary Table S1B, Supplementary Fig. S1). All these fndings suggested that nutritional status in children with marasmus may be intermediate to that of healthy children and those with kwashiorkor. Furthermore, the clustering analysis of culturomics results (Fig. 3A) showed that there was a microbiota specifc to each form of malnutrition, and confrmed the diference between kwashiorkor and marasmus. Particularly, the group of 7 children with marasmus gathered in culturomics (Supplementary Fig. S4) included children of diferent ages, genders and origins. However, the clustering was not observed in metagenomics (Figs 3B,C, S4). Tese results suggest that culturomics can be a discriminant tool to distinguish the microbiota between kwashiorkor and marasmus, without being biased, neither by age, sex nor by origin. To better describe the diference between culturomics and metagenomics approaches, we have performed a series of Venn diagram comparing the taxa identifed in each group (CTL, MRS, and KW) by culturomics and metagenomics (Supplementary Fig. S8). Te result showed there were only 16.5%, 11.4% and 18.5% of species (for all species identifed in each CTL, MRS and KW groups, respectively) that were shared between culturomics and metagenomics approaches. On the other hand, both our culturomics and metagenomics results showed that the potentially pathogenic taxa Proteobacteria and Fusobacteria were specifcally increased in kwashiorkor, while the suppression of bene- fcial taxa in Bacteroidetes occurred only in marasmus. Tus, we confrmed the complementarity of culturomics and metagenomics approaches (Fig. 4). We observed the proliferation of many Enterobacteriaceae species in kwashiorkor, such as Escherichia coli, Klebsiella oxytoca, Klebsiella pneumoniae and Hafniaceae, including Hafnia alvei (Figs 5, 6). Tese species are able to produce β-lactamase to resist penicillin28,29, suggesting that penicillin may be suboptimal for the management of kwashiorkor30. Indeed, Trehan et al. demonstrated that children receiving cefdinir (a third-generation cephalosporin) had a higher nutritional recovery rate and lower mortality rate than those who received amoxicillin2. Further studies are necessary to confrm the results of Trehan; if

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Figure 6. Increased frequency of species in kwashiorkor and marasmus samples identifed by the metagenomics approach. Mean rank values of species representing diferences in relative abundance were compared between kwashiorkor and marasmus samples. Te red bars represent an increased abundance in patients with kwashiorkor, while the green bars represent an increased abundance in Marasmus. Te grey bars represent oxygen tolerant species and the black bars represent obligate anaerobes. Proteobacteria (in orange) and potential pathogenic bacteria (p) were enriched in kwashiorkor but not in marasmus. *p-value ranging from 0.01–0.05 (Mann-Whitney test).

confrmed, cephalosporin should replace amoxicillin in the standard care regimen for severe acute malnutrition, particularly in children with kwashiorkor. Strikingly, a meta-analysis of studies analysing the treatment with antibiotics in malnourished children2,3 showed that antibiotics had a diferent efect on survival in children with kwashiorkor or without kwashiorkor (Supplementary Fig. S9). More specifcally, antibiotics highly reduced mortality in children with kwashiorkor and marasmic kwashiorkor but had no efect on the mortality in children with marasmus. Te results suggested a diference in ecosystems between children with kwashiorkor and those without kwashiorkor. In addition, we observed the abnormal depletion of Bacteroidetes in marasmus. Tis phylum includes the enterotype leaders Bacteroides and Prevotella, which are positively correlated with the fber and polysaccharides intake in the diet of rural populations31–38. Our results not only helped us pinpoint a potential specifc feature to distinguish kwashiorkor and marasmus but also may contribute to the development of nutritional therapies for children with severe malnutrition. Undoubtedly, diet components such as fbre, fat, proteins and micronutrients have a strong infuence in shaping the gut microbiota39,40. On the other hand, the gut microbiota notably contributes to the absorption of minerals and nutrient uptake40. In addition, Kismul41 showed that children with kwashiorkor consume fewer sweet potatoes and papaya, which are characterized by high β-carotene content that provides powerful antioxidant capacities for the host42,43. One of the limits of this study is the small sample size with respect to the time necessary to analyse each sample by microbial culturomics (see Methods). While a larger number of samples can be easily and quickly analysed by amplicon sequencing (v3v4 rRNA 16S gene amplicon sequencing in this study), culturomics has the unbeat- able advantage of isolating bacterial strains and identifying them much more reliably, thanks to MALDI-TOF44. Taxonomic assignment on less than 500 bp (v3v4 16S gene hypervariable region between the FwOvAd_341F and the RevOvAd_785R primers include 444 bp) is not accurate for all species. Diferent species may have the same v3v4 sequence. Here, we voluntarily carried out a culturomics study. Molecular methods were used only to test the reproducibility of our culturomics results. New attempts to shorten the time of culturomics analysis from 6 to 3 weeks per sample are ongoing using new protocols, which are currently being evaluated. Tis will increase the power and make clinical case-control culturomics studies easier, allowing to increase the sample size. Moreover,

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we demonstrated here by principal coordinate analysis that only culturomics was able to discriminate between marasmus and kwashiorkor. Te incomplete information about clinical parameters and comorbidities is another limitation of our study. As we mainly focused on the gut microbial repertoire and its variation in diferent forms of malnutrition, we neglected the assessment of many covariables of interest that could be potential confounders. Indeed, we put huge eforts on culturomics (18 standardized conditions, >10,000 colonies for each sample so that about 70,000 colonies were analysed only for this new study) and we mainly tried to compare the diferent repertoire of the digestive microbiota in marasmus, kwashiorkor and controls. In addition, we propose in the Supplementary Data a new comprehensive questionnaire for future studies on microbiota and malnutrition to discover the specifc links between clinical, biological and microbiological features in severe acute malnutrition. Such a questionnaire should include all information on medical history (history of malnutrition, previous hospitalization, malaria, meningitis); co-morbidities (malaria, HIV, tuberculosis, diarrhoea, parasites (amoebiasis, giardiasis)); breast- feeding (duration, mixed or exclusive); number of siblings; complete nutritional survey (specifcally assessing maize, carotene, zinc, fbre consumption), nutritional status when faecal samples were collected (anthropometric and clinical parameters, onset of malnutrition, frst entry in the hospital or during therapy), but also biological parameters (plasmatic concentration of protein, transferrin, albumin, glutathione, zinc) and liver characteristic (fat content of the liver assessable by non-invasive means such as ultrasound). Tis new questionnaire is provided in the Supplementary Data (Supplementary File 1). Future studies with comprehensive questionnaire identifying the environmental parameters linked to dysbiosis and nutritional features identifed in this study (total protein, transferrin, albumin, zinc, glutathione and fat content of the liver) are urgently needed to guide future preventive measures against marasmus and kwashiorkor. Such biological parameters should also be assessed in NASH since common mechanisms are suspected. For the available data: age, gender, geographical origin and period of sampling, there was no signifcant diference between the 3 groups (Supplementary Table S5). Tis suggests that our results are not biased by any of these usual potential confounders. Te inclusion of individuals from diferent countries has made it possible to analyse a greater microbial diversity. In addition, each country is represented in each group (CTL, MRS, KW) by at least 2 children, and the proportion of children from each country is not diferent among groups (Supplementary Table S5), which limits the risk of geographical bias. In conclusion, our meta-analysis results identifed four consistent plasmatic markers that were depleted in kwashiorkor compared to marasmus: albumin, transferrin, glutathione and zinc. Future studies are needed to confrm the association between biological parameters (albumin, transferrin, glutathione and zinc) with gut microbiota in children with kwashiorkor. Fatty liver was confrmed to be a specifc feature of kwashiorkor in our meta-analysis. Alteration of the gut microbiota, including a bloom of Proteobacteria and Fusobacteria and pathogenic invasion, was identifed as a specifc, instrumental factor in kwashiorkor, stressing the need for non-dietary treatments including broad-spectrum antibiotics such as cephalosporins (amoxicillin is inadequate for Enterobacteriaceae due to β-lactamase) and/or probiotic mixtures exhibiting antagonistic and regulatory activities to restore host-microbial mutualism. Methods Population and samples. Tis study continues the previously published study by Tidjani Alou et al.23, which recruited ten children with kwashiorkor and fve healthy children without any anthropometric defcit (no wasting, no stunting and not underweight)23. A further seven children diagnosed with marasmus were recruited in Senegal and Niger for our present study. Diagnosis of marasmus was made by medical staf on admission based on clinical presentation and WHO criteria4. All characteristics of study participants are detailed in Supplementary Tables S4, S5. Te number of samples was limited because of the time requirement for culturomics analysis of each sample (time to pre-incubated faecal sample afer inoculation, 30 days to follow up with total 18 diferent culture conditions were all performed corresponding to 6 weeks for each sample). All analysed stool samples were stored at −80 °C. All the parents of the children involved in this study gave an informed consent for the partici- pation of their children. Te study was approved by the ethics committee of the Institute Fédératif de Recherche 48 (IFR48), Marseille, France under agreement number 09-022. All experiments were performed in accordance with relevant guidelines and regulations of the institution.

Microbial culturomics. A total of 18 standard culture conditions (Supplementary Table S6) were used to test each sample, as previously described by Lagier et al.45. Among the 212 diferent culture conditions analysed, Lagier et al. selected the 18 culture conditions allowing to capture the maximal diversity with the minimal number of conditions23,27,45. Tese 18 culture conditions are a combination of 8 preincubation methods (blood culture bottle, blood culture bottle supplemented with rumen, blood culture bottle supplemented with sheep blood, marine broth, brain heart infusion, trypticase soy broth, Columbia-like broth supplemented with sheep blood) and 4 culture conditions (5 µ active fltration, thermic shock, temperature and atmosphere). For each condition, 1 gram of faeces was diluted in 9 mL of DPBS (Termo Fisher Scientifc, Illkirch, France); the suspension was then inoculated in the various liquid medium and incubated at a chosen atmospheric temperature. On days 1, 3, 7, 10, 15, 21 and 30, culture dilution series ranging from 1/10 to 1/1010 were inoculated on 5% sheep’s blood agar (Becton Dickinson, Le Pont de Claix, France).

MALDI-TOF identifcation. Te identifcation of isolated colonies was performed using a MICROFLEX LT spectrometer (Bruker Daltonics) and an MSP 96 MALDI-TOF target plate (Bruker Daltonics, Brenen, Germany), as previously described44. Te obtained spectra were imported into MALDI Biotyper 3.0 sofware (Bruker) and compared with databases from Bruker and the base-specifc laboratory at the La Timone hospital in Marseille, France. Te resulting score permitted the identifcation (or not) of tested species as follows: species were labelled

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as correctly identifed at the species level with a score ≥2.0 and at the genus level with a score ≥1.7 and <2.0, while no identifcation was made with a score <1.7.

16S rRNA sequencing. All strains that were not identifed by MALDI-TOF were identifed by 16S rRNA amplifcation and sequencing. Te DNA was previously extracted by the EZ1 DNA Tissue Kit using a BioRobot EZ1 Advanced XL (Qiagen, Courtaboeuf, France). Te 16S rRNA gene was amplifed via PCR by using the uni- versal primer pair consisting of fD1 and rP2 (Eurogentec, Angers, France)23. Te obtained sequences were assem- bled and corrected by using Codon Code Aligner sofware (http://www.codoncode.com) and were compared with the sequences available in the GenBank database by using BLASTn (http://blast.ncbi.nlm.nih.gov.gate1.inist. fr/Blast.cgi). A similarity threshold <98.7% was used to detect a putative new species, whereas a threshold <95% was used to detect a putative new genus23. Te confrmation and description of these putative new species is in progress according to taxonogenomics46,47.

High-throughput sequencing: metagenomics. Two protocols were used to extract DNA from stool samples48. Te frst protocol was based on a physical lysis process with glass powder, followed by an overnight chemical lysis with proteinase K. Te resulting solution was then washed and eluted according to the instructions for the Macherey-Nagel DNA Tissue Extraction Kit. Te second protocol was based on glycoprotein lysis with the addition of de-glycosylation steps. Sequencing of the DNA obtained via these two protocols was then performed targeting the V3V4 region of the 16S rRNA49. Microbial culturomics helped us decipher the various biases of metagenomics, including DNA extraction, primer bias, DNA amplifcation, and diference in bio-informatic methods and libraries27. In a previous study, we showed that glycoproteins afect DNA extraction, and that a protocol including a deglycosylation step yields the maximal diversity as observed with rarefaction curves48. Tis protocol was named P5 and was used with the standard QIAGEN protocol (P1), which is more rapid and yields diferent microbes. Tese protocols have been chosen once and all samples analysed in our centre use the same protocols, for reproducibility48. Raw sequencing data was available from Nucleotide Sequence Database EMBL-EBI (Reference: PRJEB31774). Further information on the accession number of each sample is described in Supplementary Table S2.

Diversity assessment. β-diversity is the number of taxa that are unique to each ecosystem and is used to compare the species diversity between ecosystems along environmental gradients50. In each group (CTL, MRS, KW) beta diversity was assessed by comparing the number of species found in a single individual with the number of species found in the entire group. Tus, the unique /total ratio (U/T ratio) was 0 if all individuals had an identical microbiota and 1 (100%) if each individual in the group had specifc species without any species shared between the diferent individuals in the group23. For the metagenomics results, we frst created a workfow, which is depicted in Supplementary Fig. S10. To reduce the number of false-positive hits and increase the accuracy of abundance estimates, we used a normalization method (rarefying of counts)51. An analysis of the rarefaction curves based on OTUs (Supplementary Fig. S11) showed a clear diference between the marasmus and kwashiorkor groups, and was almost constant from 5,000 reads up to 60,000 reads. Tis suggested that a rarefaction at 60,000 reads was relevant to compare the gut microbiota diversity of children with marasmus and kwashiorkor. Terefore, we picked 60,0000 reads as a threshold value afer reaching the minimum total number of reads in each sample, which allowed us to maintain all 22 individual patient’s samples afer normalizing. Consequently, all relative abundances <1/60,0000 were considered 0. All OTUs detected in each sample of each group were described in Supplementary Table S7. At the species level, the comparison between two groups was assessed by calculating and comparing the mean rank in each group52. Second, the Shannon index was also calculated to assess global, anaerobic and aerotolerant diversity by using the following formula: H’ = ∑pi*log2pi, where pi is the proportion of each species in the sample for which diversity is being estimated53. Both culturomics and metagenomics results were compared to each other to confrm our results and highlight the complementarity between these two methods.

Statistical analysis. RevMan v5.3 (Copenhagen: The Nordic Cochrane Centre, The Cochrane Collaboration, 2014) was used to perform a meta-analysis, with random-efects model. Tests for heterogeneity is used to demonstrate the consistency/inconsistency between the several studies analysing the diferences between kwashiorkor and marasmus. To compare the means between two populations, two-tailed Student’s t-test and two-tailed Mann-Whitney U-test were used afer testing for normality (Gaussian distribution). Te bilateral exact Fisher test and uncorrected Chi-squared test were used to compare proportions. Correction for the false discovery rate was not necessary since this was an exploratory study54. As our prespecifed hypothesis included a gradient, and we expected children with marasmus to have intermediate values between those of controls and children with kwashiorkor, we sought to test this gradient by statistical analysis. A linear regression model (ANOVA test) was used to test gradients for quantitative variables, and group value was the independent variable. Te Cochran-Armitage trend test was used for proportions55,56. Statistical analyses were performed with SPSS v22.0 (IBM, Paris, France) and XLSTAT Biomed v19.5 (Addinsof, Paris, France).

Ethics committee approval. Te study was approved by the ethics committee of the Institute Fédératif de Recherche 48 (IFR48), Marseille, France under agreement number 09-022.

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