The impact of residential villages in a karstic watershed on numbers and diversity of nontuberculous mycobacteria

doi:10.21203/rs.3.rs-3292601/v1

The impact of residential villages on the nontuberculous mycobacteria (NTM) in streams flowing through them has not been studied in detail. Water and sediments of streams are highly susceptible to anthropogenic inputs such as surface water flows. This study investigated the impact of 7 residential villages in a karst watershed on the prevalence and species spectrum of NTM in water and sediments. Higher NTM species diversity (i.e., 19 out of 28 detected) was recorded downstream of the villages and wastewater treatment plants (WWTPs) compared to sampling sites upstream (i.e., 5). Significantly higher pH and Zn concentration were detected in sediments inside the village and downstream of the WWTP’s effluents. P and Cu sediment concentrations were higher only downstream of WWTPs compared to sediments inside the settlement. The effluent from the WWTPs had a substantial impact on water quality parameters with significant increases in total N, P, anions (HCO₃^–, Cl^–, N-NO₃^–, and N-NH₃^–), and cations (Na⁺ and K⁺). The results provide insights into NTM numbers and species diversity distribution in a karst watershed and the impact of urban areas. Although in this report the focus is on the NTM, it is likely that other water and sediment microbes will be influenced as well.

Village impacts on rivers

Mycobacterium avium

Mycobacterium fortuitum

Wastewater treatment effluent impacts

Human activities

mycobacterial ecology

Nontuberculous mycobacteria (NTM) are widely distributed in the environment [1, 2]; typically, in soil [3, 4] and dust [5] followed by surface water sediments [6], indoor water biofilms [7], surface water biofilms [8], small-scale distributed water purifiers [9], in free-living amoebae isolated from wastewater [10], and other matrices [11, 12, 13, 14]. Due to the high surface hydrophobicity of NTM cells, NTM densities are higher in biofilms and sediments compared to water [8, 15]. NTM have an important role in degrading of organic compounds arising from anthropogenic activity [16, 17, 18] and thus, might be found in higher numbers in villages compared to surrounding forests. Due to these characteristics, NTM can be considered “persistent bacterial indicators of environmental influences” [19, 20].

Limited information is known about the impact of small villages on NTM prevalence in surface water and water sediments in minor water courses. In this study, we focused our interest on a specific karstic watershed. Genus Mycobacterium was detected in karst cave environment for the first time in 1994 [21]. A karstic watershed has the following characteristics including (1) engulfed streams in karst landscape and (2) underground water channels, caves, and sinkholes due to the solubilisation of limestone. These factors make karstic caves and aquifers highly vulnerable to anthropogenic contamination [22, 23, 24, 25, 26, 27]. Focused on microbial contamination, special attention in karstic water has been devoted to the transport of pathogenic Escherichia coli and faecal indicator bacteria [28, 29, 30, 31, 32, 33, 34, 35, 36].

We hypothesized that water and wastewater originating from villages’ households would trigger a broader diversity of NTM spreading detected in cave sediments in one karst area (Moravian Karst, Moravský kras; Czech Republic). Thus, this study aimed to record the species and types NTM in karstic water and water sediments both upstream or downstream villages and treated water from wastewater treatment plants (WWTPs). In addition, we measured the physical and chemical parameters of the water and sediment samples, along with the number and species of NTM from samples collected upstream, within, and downstream of the villages and their WWTPs outflows of seven selected villages.

Study area

The study was carried out in the Moravian Karst (Czech Republic) with its Protected Landscape Area (PLA) of approximately 92 km² established in 1956 (Fig. 1A) [37] and surrounded watershed area in the southeast of the Czech Republic. That region has the two most extensive cave systems in the Czech Republic. Amaterská jeskyně Cave System is long; more than 34 km and cave system created by linking Rudice Swallow Hole (Rudické propadání), Bull Rock Cave (Býčí skála), and Barová Cave (Barová jeskyně) is long as well; than 13 km (Fig. 1B) [37]. The Moravian Karst PLA is in a cold to moderately warm climatic region according to Quitt's Classification; average annual precipitation reaches 550–650 mm and average annual air temperature ranges between 5–7°C [38]. The studied area was defined by watersheds created by four streams: 1. Bílá voda Stream, 2. Punkva Stream, 3. Jedovnický potok Stream, and 4. Říčka Stream and their tributaries, whose watersheds encompass approximately 283 km².

Sampling design

Sampling sites were located in seven villages (villages; V1–V7; Figs. 1A–B) with 318–1,117 population [39] based on followed criteria:

1. Collected from a minor flow of the river within the village (average flow rate Q = 0.015–0.500 m^− 3/s),
2. Each village was the first village downstream of the watercourse,
3. Collected water and sediment from waters in which chlorine compounds were used for treating drinking water used in households, and
4. Collected from every village treated wastewater downstream from the WWTP or other type of wastewater treatment.

The water and water sediments were sampled in three different sampling sites (SS):

1. SS-A: upstream of the particular village (300–1,500 m),
2. SS-B: within the confines of the village (100–300 m) up to WWTPs and
3. SS-C: downstream of the village and the WWTP’s outflows (10–30 m downstream of the WWTPs outflows).

Following seven villages with watercourses were sampled (Figs. 1A–B): V1 – Žďár (water stream: Žďárský potok), V2 Vysočany (water stream: Bělička), V3 Niva (water stream: Bílá voda), V4 Ostrov u Macochy (water stream: Lopač), V5 Kotvrdovice (water stream: Kotvrdovický potok), V6 Podomí (water stream: Podomský potok), and V7 Březina (water stream: Ochozský potok). A total number of sampling sites was 21.

Water and water sediments from the streams (42 samples) for physicochemical analyses were sampled two times per year (during the spring and the autumn seasons). Water sediments for NTM identification (84 samples) were taken two times per two years (collected in total 4 times). Water was collected in 0.5 L sterile plastic disposable bottles. The water sediments (i.e., 30 g) were taken at depths of 0–3 cm directly into a sterile 60 mL PP sputum container (DISPOLAB s.r.o., Troubsko, Czech Republic) for mycobacterial investigations and using equipment designed for water sediment to plastic storage bag in amount 200 g for physical and chemical analyses. Samples were transported in a cold box to laboratories and stored in a refrigerator at 6°C till the analyses. Field measurements of water parameters were not conducted upstream of the villages (SS-A) due to less reachable terrain for analytic equipment.

Mycobacterial culture and isolates identification

Water samples (1 000 mL) were filtered through 0.45 µm Teflon filters (Millipore, Merc, Molsheim, France). Each filter was transferred to a 30mL container filled with 10 mL of distilled water and around of 20 pieces of glass beads (2 mm) and vortexed 30 sec. The filter was discarded, and the eluate decontaminated to avoid overgrowth of the naturally slow growing mycobacterial culture by substantially fast-growing microbial flora consisting of other common bacteria and fungi [42, 43]. Each water sediment sample (maximum of 10.0 g) was diluted with distilled water up to 30 mL and vigorously shaken for 10 min to obtain maximal suspension homogeneity and to dissolve large soil clusters, and release mycobacterial cells. Then it was centrifuged for 10 min at approx. 150 x g to allow for water sedimentation of insoluble particles and most of the residual material, which could hamper the decontamination process as they interact with the chemicals and weaken their effect [44, 45]. A total of 10 mL of each turbid supernatant was transferred to a new centrifuge tube and centrifuged for 20 min at 3 200 x g. Pellets were homogenized and decontaminated with 4% NaOH in the 1:1 mixed solution with 1% alkyl-trimethylammonium bromide (cetrimide) as described previously [42, 43]. A total of 800 µL of suspension (re-suspended pellet) per 200 µL was inoculated in duplicate into two slants with Lowenstein-Jensen medium without and with sodium-pyruvate. Incubation was done in parallel for three months at 28°C and 37°C. Mycobacterial growth was examined after the first week and every other week [40, 41].

All suspected NTM isolates were first identified by macroscopic and by microscopic (Ziehl-Neelsen staining) examinations [40, 41]. Identification was done by AccuProbe Test (Hologic, Inc., San Diego, CA, USA), by PCR with reverse hybridization on cellulose strips GenoType Mycobacterium CM/AS assays (Hain Lifescience GmbH, Nehren, Germany), or by sequencing the 16S rRNA gene (Applied Biosystems Genetic Analyzer ABI3130 series, Thermo Fisher Scientific, Inc., Waltham, MA, USA).

Physical and chemical analyses

Water parameters: temperature, pH, dissolve oxygen concentration (DOC), oxidation-reduction potential (ORP), and electrical conductivity (EC) were measured at the place and time of sampling. The temperature and pH were determined by measurement device GMH 5530 with GE 117 electrode (GHM Messtechnik GmbH, Standorf Greisinger, Germany). EC was measured by GMH 5450 equipment with LF 425 electrode and DOC was determined by GMH 3630 with integrated temperature and pressure measuring. WTW InoLab Multi 720 tool with a SenTix 41 electrode (WTW Ltd, Prague, CR) was used during ORP measurements.

Chlorides and sulphates were measured by HPLC using the Dionex ICS-2000 Ion Chromatography System (ICS-2000, Thermo Fisher Scientific, Inc., Waltham, MA, USA) with IonPac® AS 18 analytical (2 x 250 mm) column. Acid Neutralization Capacity to pH 4.5 (ANC_4.5) and alkalinity were determined by titration with 0.1 N hydrochloric acid. Alkalinity was performed using potentiometric titration according to the ISO 9963-1:1994 (Water quality – Determination of alkalinity – Part 1: Determination of total and composite alkalinity). Carbonate (HCO₃^–) concentrations were calculated based on the results of (ANC_4.5) and alkalinity. Total nitrogen was measured by chemiluminescence using Total Organic Carbon Analyzer and Total Nitrogen Measuring Unit (VCPH, Shimadzu Corporation, Japan). Individual nitrogen forms (N-NH₃ and N-NO₃^–) were measured by continuous flow analysis using the Skalar San Plus (Skalar Analytical B.V., Breda, The Netherlands). Calcium (Ca), magnesium (Mg), sodium (Na), potassium (K), iron (Fe), and phosphorus (P) were detected by inductively coupled plasma mass spectrometry (ICP-MS) using ICP-MS Agilent Technologies 7700 (Agilent Technologies Inc., Colorado Springs, CO, USA).

The physicochemical parameters of sediments were measured in water extracts after drying at 60°C to constant weight. ISO methods were used for to measure pH, EC, total organic carbon (TOC), and total inorganic carbon (TIC). WTW InoLab Multi 720 tool with a SenTix 41 electrode (WTW Ltd, Prague, CR) was used for pH measurements (ISO 10390:2005 - Soil quality - Determination of pH). For EC measurements, water sediment aliquots were mixed with distilled water (1:5), shaken for 30 min, and measured on a multimeter WTW Multi 3320 with a TetraCon 325 electrode (ISO 11265:1994 - Soil quality - Determination of the specific electrical conductivity). TOC and TIC in the water sediment samples were analysed on an Analytik Jena multi N/C 2100S meter equipped with the module HT 1300 (Analytik Jena AG, Jena, Germany) according to the standard method (ISO 10694:1995 - Soil quality - Determination of organic and total carbon after dry combustion; elementary analysis). Calcium carbonate and tetrasodium ethylenediamine tetraacetate tetrahydrate (EDTA) were used for calibrations. The homogenized samples (10 g of dry sample) were weighed and incinerated at 1400°C with the final CO₂ concentration reported as a peak in direct proportion to total carbon (TC). Samples containing carbonates were then acidified with hydrochloric acid before being analysed for TOC and TIC was calculated as the difference between TC and TOC.

Total nitrogen was measured by the Dumas Combustion Method using a Dumatherm Analyzer (C. Gerhardt GmbH & Co. KG, Koenigswinter, Germany). NO₃ and N-NH₃ were extracted from samples by K₂SO₄ and analysed by using spectrophotometry (N-NO₃ – 324 nm; N-NH₃ – 655 nm). Microelements (aluminium, arsenic, cadmium, calcium, copper, chromium, iron, lead, manganese, molybdenum, nickel, phosphorus, potassium, rubidium, silicon, silver, sulphur, strontium, thallium, titanium, uranium, vanadium, zinc, and zirconium) were analysed by X-ray analysis using XRF Innov-X Systems, Inc. Value 0.25 × 1 σ was added in the case of value lower than the limit of detection (LOD).

Statistical analysis

Data of physicochemical parameters of water and water sediments were evaluated using one-way analysis of variance (ANOVA) with the sampling site type factor (SS-A, SS-B, and SS-C), resp. t-test with Welch's correction (EC) or Mann-Whitney test (temperature, DO, and ORP). To achieve a normal distribution and homogeneity of variances, the data were logarithmically transformed using before analysis. For the water parameters, the transformation did not need to be used only for the EC, calcium, and magnesium concentrations. In the case of total nitrogen, the approximate homogeneity of the variances was not achieved even after the use of the transformation, so that the Welch's modification of the ANOVA was used instead of the classical ANOVA.

Transformation was not needed for ash and pH, but for TC, the approximate normality was not achieved even after the use of the transformation, so the non-parametric Kruskal-Wallis test was used. If the hypothesis of equality of group means (SS-A, SS-B, and SS-C) was rejected (ANOVA P-value < 0.05), Tukey's HSD post-hoc test was performed. For the total nitrogen parameter, the Games-Howell test was used as the post-hoc test.

The alpha-diversity of NTM sp. and ssp. isolated from three sampling sites (SS-A, SS-B, and SS-C) samples were assessed using the Shannon’s diversity index H [46]. The Hutcheson test was used to compare H-indexes according to the origin of the samples. The beta-diversity of NTM sp., ssp., and complexes isolated from these three sampling sites samples (SS-A, SS-B, and SS-C) was assessed by paired PERMANOVA (Bray-Curtis dissimilarity, 9999 permutations).

Data analysis was performed using statistical software Statistica 13.2 (StatSoft Inc., Tulsa, OK, USA) and GraphPad Prism 5.04 (GraphPad Software Inc., San Diego, CA, USA). P-values less than 0.05 were considered statistically significant.

A Fisher’s exact test was performed to determine whether there were significant differences in NTM positivity between different locality type (GraphPad Prism v5.02; GraphPad Software, Inc., USA).

Mycobacteria in water samples

NTM were rarely recovered from water samples. Only 3 (7.1%) out of a total of 42 water samples yielded five NTM isolates (from two samples two different NTM were isolated) belonging to three sp. and one complex: M. terrae (1 isolate), M. avium complex (1 isolate), M. fortuitum (2 isolates), M. septicum (1 isolate), and M. terrae (1 isolate). NTM were cultured only from the water samples collected in three localities (V5–7) from sampling sites collected within the village and downstream of the WWTPs outflows (SS-C; Table 1).

Mycobacteria in sediment samples

Compared to the water samples, sediment samples yielded more NTM; specifically, 48.8% of the 41 water sediments yielded NTM. There were no statistically significant differences in NTM yields between different sampling sites: 50.0% upstream (SS-A), 42.9% inside the village and upstream to the outflow of the WWTPs (SS-B), and 53.6% downstream of the village’s WWTP outflows (SS-C). A total of 28 NTM isolates representing different species, subspecies or complexes were identified. M. fortuitum (8 isolates) and M. avium ssp. hominissuis (8 isolates) were the most frequently detected NTM found in all sampling sites (Table 1).

Mycobacteria species diversity in water and sediment samples

The NTM species diversity was significantly lower (P < 0.01; Hutcheson test for Shannon’s H index) in samples collected upstream of the villages (SS-A) with the detection of only five NTM sp. and ssp. (7 isolates): M. avium ssp. hominissuis (2 isolates), M. intracellulare (1 isolate), M. flavescens (1 isolate), M. fortuitum (2 isolates), and M. psychrotolerans (1 isolate) compared to sampling sites within the villages to the WWTPs (SS-B; 15 NTM sp. and ssp., 19 isolates) and sampling sites within the village, and downstream of the WWTPs outflows (SS-C; 19 NTM sp. and ssp., 29 isolates). The NTM positivity was significantly higher in sampling sites within the village and downstream of the WWTPs outflows in comparison to upstream of the villages (Fisher’s exact test, p < 0.001). NTM positivity did not differ with

Table 1

Isolated nontuberculous mycobacteria from 42 water (two repeated collections) and 84 sediment samples (four repeated collections) collected in 7 villages (V1–7) from three sampling sites A, B, and C.
Risk	Ge-	Species, Subspecies, and	No. of	V1–7			V1			V2			V3			V4			V5			V6			V7
Group	nus	Complexes	Isolates	A	B	C	A	B	C	A	B	C	A	B	C	A	B	C	A	B	C	A	B	C	A	B	C
	M.	arcueilense	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1
	M.	chlorophenolicum	2	0	1	1	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	M.	chubuense *WS	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	M.	duvalii	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1
	M.	gilvum *WS	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
	M.	gordonae *F	4	0	1	3	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	2
1	M.	hassiacum	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
	M.	insubricum *WS, F, B, C	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
	M.	kumamotonense **CS	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1
	M.	montmartrense	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1
	M.	psychrotolerans	2	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0
	M.	rhodesiae *B	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
	M.	terrae *WS, F, B, C	2	0	1	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1^W	0	0	0	0	0	0
	M.	triviale	3	0	1	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	2
	Subtotal No. of isolates		22	1	8	13	0	0	0	0	1	2	0	0	0	0	1	0	0	2	1	1	1	2	0	3	8
	M.	arupense *WS	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
	M.	avium complex	2	0	0	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1^W	0	0	1	0	0	0
	M.	avium ssp. hominissuis *WS, B, F	8	2	3	3	0	0	0	0	2	2	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0
	M.	flavescens *WS	1	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
	M.	fortuitum *WS, F, B, C	8	2	3	3	0	0	0	0	0	1	1	0	0	1	1	0	0	0	0	0	1	1^W	0	1	1^W
	M.	intracellulare	1	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	M.	monacense **CS	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
2	M.	mucogenicum	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
	M.	parascrofulaceum *WS, F, B	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
	M.	peregrinum *F, C	3	0	1	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	1
	M.	porcinum *F, B	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1
	M.	septicum	3	0	1	2	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	1^W
	M.	smegmatis	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1
	M.	xenopi *B, C	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
	Subtotal No. of isolates		33	6	11	16	0	0	0	1	2	3	2	1	2	3	1	0	0	1	2	0	3	4	0	3	5
NK	M.	species	17	9	3	5	2	0	1	1	1	1	1	0	1	1	1	1	0	1	0	2	0	0	2	0	1
	Total No. of isolates		72	16	22	34	2	0	1	2	4	6	3	1	3	4	3	1	0	4	3	3	4	6	2	6	14
1 + 2	Species, Subspecies, and Complexes		28	5	15	19	0	0	0	1	2	4	2	1	2	3	2	0	0	3	3	1	4	6	0	6	11

M. = Mycobacterium; Risk Group 1 of Agents (low individual and community risk) = includes those microorganisms, bacteria, fungi, viruses and parasites, which are unlikely to cause disease in healthy workers or animals.), Risk Group 2 of Agents (moderate individual risk, limited community risk) = includes pathogens that can cause human or animal disease but under normal circumstances, is unlikely to be a serious hazard to healthy laboratory workers, the community, livestock, or the environment according to the European Union Directive 2000/54/EC (Risk groups are taken from LPSN https://lpsn.dsmz.de/) [68]; V1–7 = villages 1–7 (V1 = Žďár, water stream: Žďárský potok; V2 = Vysočany, water stream: Bělička; V3 = Niva, water stream: Bílá voda; V4 = Ostrov u Macochy, water stream: Lopač; V5 = Kotvrdovice, water stream: Kotvrdovický potok; V6 = Podomí, water stream: Podomský potok; V7 = Březina, water stream: Ochozský potok), A, B, and C = sampling sites: A = upstream (300–1,500 m) the village, B = inside the village (100–300 m) up to wastewater treatment plants (WWTPs), and C = downstream the outflow of WWTPs (10–30 m downstream of the WWTPs outflow); ^W water samples were positive only; NK = not known; * found in Bull Rock Cave (Býčí skála) located in Moravian Karst (Moravský kras) in previous study by Modra et al. [47]: WS = water sediments and/or alluvial material, F = bats faeces and/or earthworms castings in the cave, B = inside the village up to WWTPs in Rudice and Jedovnice, C = downstream the outflow of WWTPs in Rudice and Jedovnice, ** found in Amaterská Cave System located in Moravian Karst in previous study by Modra et al. [77 − 70]: CS = cave sediment contaminated by dripping water; numbers of isolates in gray colour are showing the species or subspecies isolation from different sampling sites in the same village by Pavlik et al. [48].

the village and downstream of the WWTPs outflows. Six NTM species were widely distributed throughout the rivers; namely, M. avium ssp. hominissuis, M. chlorophenolicum, M. fortuitum, M. peregrinum, M. septicum, and M. triviale. They were isolated from samples collected from different sites in the same village: the numbers of isolates (grey colour) showing these sp. and ssp. isolates from other sampling sites in the same village. The studies reported here agree with prior studies of NTM prevalence and species diversity. Specifically, in the Moravian Karst region studied here, two previous studies reported NTM species in Bull Rock Cave (Býčí skála) and Amaterská Cave System [47, 48].

Water physiochemical characteristics

Field measurements showed lower water sample oxygen concentrations (P < 0.05; Mann-Whitney test) and higher conductivity (P < 0.01; Mann-Whitney test) in water within the village and downstream of the WWTP outflows (SS-C) compared to the water in the village upstream of the WWTPs in SS-B (Table 2).

Table 2

Physicochemical characteristics of water from field measurement in SS-B = sampling site B in the village up to wastewater treatment plants (WWTPs) and SS-C = sampling site C in the village downstream the WWTPs, (n = 14, mean ± SD), DOC = Dissolved Oxygen Concentration, ORP = Oxidation-Reduction Potential, and EC = Electrical Conductivity, significant differences by Mann-Whitney test are marked in bold.
Parameter	SS-B	SS-C	Mann-Whitney test (p-value)
pH	7.39 ± 0.38	7.17 ± 0.30	0.3134
Temperature [°C]	6.72 ± 2.98	8.59 ± 1.93	0.1155
DOC [mg/L]	11.12 ± 1.04	7.35 ± 2.42	0.0051
ORP [mV]	230.50 ± 12.49	231.60 ± 37.02	0.8896
EC [µS/cm]	349.15 ± 157.66	644.82 ± 317.09	0.0138

Significantly higher (P < 0.05 at least; ANOVA, Tukey’s HSD post-hoc test) concentrations of total nitrogen (p < 0.001), ammonia (p < 0.01), nitrates (p < 0.001), chlorides (p < 0.001), potassium (p < 0.001), phosphorus (p < 0.001), and sodium (p < 0.001) were found in water under the village, downstream of the WWTP-outflows (SS-C) compared to the other two sampling sites upstream of the villages (SS-A) and in the village down to WWTPs (SS-B). Higher acid neutralization capacity to pH 4.5(ANC_4.5; p < 0.05) and carbonate concentrations (p < 0.05) were recorded in samples collected under the village, downstream of the WWTP outflows (SS-C) compared to upstream of the villages (SS-A; Fig. 2). No differences (P > 0.05; ANOVA) were found between all three sampling sites (SS-A, SS-B, and SS-C) in sulphates, calcium, iron, and magnesium concentrations. However, significant differences (P < 0.05 at least; ANOVA, Tukey’s HSD post-hoc test) were observed for nitrogen, phosphorus, potassium, and sodium (Table 3).

Sediment physiochemical characteristics

Sediments showed statistically significantly higher pH (p < 0.01) in the village samples up to WWTPs (SS-B) compared to samples collected within the village, downstream of the WWTPs outflows (SS-C) and upstream (SS-A). Concentrations of calcium, copper, phosphorus, and zinc in sediments collected within the village and downstream of the WWTPs outflows (SS-C) were significantly higher (p < 0.05) than other sampling sites SS-A and SS-B.

Table 3

Physicochemical parameters of water (mean ± SD) and results of statistical analysis. Significant differences (p < 0.05) between sampling site are marked by different alphabetic superscripts. η² = ANOVA effect size; ¹ Welch modification of ANOVA and Games-Howell post-hoc test; ANC_4.5 = Acid Neutralization Capacity to pH 4.5; SS-A = sampling site upstream (300–1,500 m) the village, SS-B = sampling site inside the village (100–300 m) up to wastewater treatment plants (WWTPs), and SS-C = sampling site under the village, downstream the outflow of WWTPs (10–30 m downstream of the WWTPs outflow); ANC_4.5 = Acid Neutralization Capacity to pH 4.5.
Parameter	ANOVA (p-value)	η²	Sampling Site
Parameter	ANOVA (p-value)	effect size	SS-A (n = 14)	SS-B (n = 14)	SS-C (n = 14)
ANC_4.5	0.0197	0.1742	2.308 ± 0.632^a	3.047 ± 0.931^ab	3.407 ± 1.234^b
Hydrocarbonates [mg/L]	0.0151	0.1849	138.9 ± 39.99^a	185.8 ± 56.87^ab	207.77 ± 75.33^b
Chlorides [mg/L]	0.0007	0.2964	26.97 ± 19.11^a	31.60 ± 19.52^a	60.45 ± 35.34^b
Sulphates [mg/L]	0.5894	0.0255	77.22 ± 49.77	72.92 ± 69.67	68.73 ± 31.57
Total nitrogen [mg/L] ¹	0.0000	0.4443	1.911 ± 0.408^a	1.924 ± 0.834^a	8.568 ± 7.010^b
N-NH₃ [mg/L]	0.0058	0.2221	0.116 ± 0.083^a	0.294 ± 0.540^a	1.991 ± 3.415^b
N-NO₃^– [mg/L]	0.0007	0.2978	0.852 ± 0.800^a	0.894 ± 0.875^a	4.867 ± 5.260^b
Calcium [mg/L]	0.3239	0.0535	49.62 ± 16.59	65.74 ± 33.45	61.47 ± 21.66
Iron [mg/L]	0.9185	0.0041	0.612 ± 0.684	0.447 ± 0.378	0.948 ± 0.723
Magnesium [mg/L]	0.6974	0.0174	12.68 ± 3.360	13.31 ± 5.844	12.01 ± 3.725
Phosphorus [mg/L]	0.0000	0.5354	0.203 ± 0.121^a	0.185 ± 0.122^a	1.763 ± 1.865^b
Potassium [mg/L]	0.0000	0.5722	4.202 ± 1.350^a	3.959 ± 2.316^a	13.20 ± 6.701^b
Sodium [mg/L]	0.0000	0.4179	23.42 ± 9.236^a	20.77 ± 10.74^a	52.25 ± 29.89^b

The N-NH₃ concentrations in water sediments was lower (p < 0.05) in villages up to WWTPs (SS-B) compared to the water sediments collected upstream of the villages (SS-A). The silver concentration was lower (p < 0.05) in the water sediments inside villages up to WWTPs (SS-B) compared to other sampling sites SS-A and SS-C. The highest concentration of thallium (p < 0.05) was detected upstream (SS-A). No statistical differences were recorded in the rest of chemical parameters in water sediments collected in all sampling sites (Table 4).

Water as transport medium of mycobacteria

The relatively low frequency of NTM in water samples (7.1%) compared to sediments (48.8%) suggests that particulate-associated NTM cells are the major sources of pathogenic NTM. These data are in accordance with previously published studies [8, 50, 51, 52, 53]. Drinking water reservoirs with biofilms and sediments, therefore, represent a significant source of NTM [54, 55, 56]. The range of sample types did not allow us to determine the individual contributions of NTM isolates in samples collected in villages or downstream of villages and waste water treatment plants. Both village surface water runoff and sewage plant effluents will enter the streams and contribute to both numbers and diversity of NTM.

Table 4

Physicochemical parameters of water sediments and results of statistical analysis. Significant differences (p < 0.05) between localities are marked by different alphabetic superscripts. η² = ANOVA effect size; SS-A = sampling site upstream (300–1,500 m) the village, SS-B = sampling site inside the village (100–300 m) up to wastewater treatment plants (WWTPs), and SS-C = sampling site downstream the outflow of WWTPs (10–30 m downstream of the WWTPs outflow); TC = Total Carbon; TOC = Total Organic Carbon; n = 14 (parameters: Total Nitrogen, N-NH₃ and N-NO₃₋ n = 7).
Parameter	ANOVA (p-value)	η²	Sampling Site
Parameter	ANOVA (p-value)	effect size	SS-A (n = 14)	SS-B (n = 14)	SS-C (n = 14)
Ash	0.5080	0.0273	45.95 ± 15.20	50.90 ± 18.07	54.29 ± 24.86
Conductivity	0.3671	0.0417	154.5 ± 49.76	243.1 ± 152.4	320.56 ± 348.6
pH	0.0037	0.2081	6.441 ± 0.350^a	6.768 ± 0.219^b	6.717 ± 0.256^b
TC	0.3952		9.612 ± 12.76	5.806 ± 3.097	5.504 ± 3.713
TOC	0.1290	0.0835	7.419 ± 7.437	4.665 ± 2.272	4.532 ± 3.225
Total nitrogen [%]			0.327 ± 0.370	0.105 ± 0.062	0.395 ± 0.696
N-NH₃ [mg/kg]			29.55 ± 37. 34^b	15.82 ± 12.00^a	29.45 ± 27.73^ab
N-NO₃^– [mg/kg]			13.35 ± 7.922	12.89 ± 9.49	18.02 ± 11.02
Phosphorus [µg/kg]			402.8 ± 556.4^a	412.2 ± 324.3^a	1681.5 ± 2618^b
Calcium [mg/kg]			7.066 ± 7.400^a	7.429 ± 7.618^a	31.88 ± 57.80^b
Iron [mg/kg]			27.90 ± 6.839	25.65 ± 6.179	25.02 ± 6.347
Aluminium [mg/kg]			43.72 ± 5.618	43.92 ± 6.40	38.44 ± 7.13
Silicon [mg/kg]			225.7 ± 13.73	216.0 ± 16.86	201.9 ± 51.20
Sulphur [µg/kg]			381.0 ± 569.5	182.6 ± 113.8	1480 ± 2235
Potassium [mg/kg]			15.93 ± 1.901	15.54 ± 1.511	15.23 ± 3.110
Titanium [mg/kg]			4.402 ± 0.690	4.182 ± 0.613	3.863 ± 0.954
Vanadium [µg/kg]			130.3 ± 34.88	130.5 ± 25.11	126.0 ± 32.37
Chromium [µg/kg]			31.41 ± 5.937	35.88 ± 11.37	31.23 ± 6.212
Manganese [mg/kg]			1.487 ± 1.610	0.984 ± 0.387	1.058 ± 0.869
Nickel [µg/kg]			20.67 ± 11.09	19.80 ± 13.74	17.60 ± 6.70
Copper [µg/kg]			24.60 ± 23.14^a	28.32 ± 11.06^a	45.53 ± 40.19^b
Zinc [µg/kg]			116.3 ± 62.21^a	152.4 ± 52.82^a	198.2 ± 121.8^b
Arsenic [µg/kg]			15.28 ± 4.125	13.68 ± 4.750	13.39 ± 5.024
Rubidium [µg/kg]			104.7 ± 8.616	103.7 ± 11.07	97.99 ± 17.97
Strontium [µg/kg]			123.9 ± 23.30	151.8 ± 43.19	147.9 ± 41.39
Zirconium [µg/kg]			259.5 ± 83.09	212.9 ± 100.2	198.3 ± 92.93
Molybdenum [µg/kg]			5.422 ± 1.426	4.200 ± 0.971	5.214 ± 1.917
Silver [µg/kg]			16.58 ± 5.523^b	11.07 ± 1.567^a	20.09 ± 7.922^b
Cadmium [µg/kg]			14.91 ± 3.748	13.32 ± 2.838	14.72 ± 4.181
Lead [µg/kg]			30.81 ± 6.211	30.37 ± 8.527	28.72 ± 8.263
Thallium [µg/kg]			36.36 ± 14.74^b	31.43 ± 7.684^a	30.85 ± 18.99^a
Uranium [µg/kg]			6.774 ± 3.492	4.595 ± 1.235	7.112 ± 4.340

The chlorine and disinfectant resistance of members of the genus Mycobacterium [57] permit survival and proliferation of NTM in drinking water distribution systems [58]. Biofilms in any water system, whether in nature or engineered systems, are the primary habitats of NTM [7, 59]. Most of the NTM detected in sediments were cultured from upstream, within the village, or downstream of the WWTPs outflows (SS-B and SS-C) sediments (Table 1). The source of these NTM could be explained by reported NTM abundance in drinking water and household plumbing systems [7, 11, 59, 60].

E.g., in the Hawaiian Islands, NTM detection from home plumbing systems was significantly higher than NTM detection from outdoor environmental water biofilms [7]. In other studies, they also found that biofilms in drinking water pipes are also richly colonized by various NTM species [59, 60].

Impact of villages on mycobacteria species diversity

Both surface-runoff and sewage plant outflows associated with villages are possible sources of NTM. Possibly, the introduction of water from those sources plays an important role in NTM spreading in surface water environment [63]. This was found in our study also. Significantly higher (P < 0.05 at least; PERMANOVA) numbers of NTM sp. and ssp. in water sediments in the villages up to WWTPs (SS-B) and under the village, downstream of the WWTPs outflows (SS-C) in comparison with sampling sites above village were detected (SS-A; Table 1). Higher NTM sp. and ssp. diversity in these ecological niches (SS-B and SS-C) could also be connected with higher temperature caused by wastewater from households (mentioned above) and WWTPs [64, 65, 66, 67].

Although we did not focus our study on WWTPs technologies, we can conclude that wastewater (sampling sites SS-B and SS-C) had affected NTM diversity, which was the same in these both polluted sampling sites but was statistically significantly lower in non-polluted water sediments upstream of the villages (SS-A; Table 1). In light of the proposed use of treated wastewater (i.e., reuse water), the findings here of NTM in WWTP effluent need to be taken into consideration. Recycling wastewater for human, animal, or agricultural production might increase exposure to NTM due to their ability to survive disinfection [57].

Prevalence of mycobacteria and human health risk

A total of 195 mycobacterial sp. and ssp. are classified into three Risk Groups (1, 2, and 3) of biological agents according to the classification taken from the German TRBA (Technical Rules for Biological Agents) 466 downloaded on July 1st 2022 and according to EU directive 2000/54/EC on the protection of workers from risks related to exposure to biological agents at work [68]. According to this European Union Directive 2000/54/EC the Risk Group 1 of Agents includes those microorganisms, bacteria, fungi, viruses and parasites, which are unlikely to cause disease in healthy workers or animals (low individual and community risk). The Risk Group 2 of Agents includes pathogens that can cause human or animal disease but under normal circumstances, is unlikely to be a serious hazard to healthy laboratory workers, the community, livestock, or the environment (moderate individual and limited community risk). The Risk Group 3 includes microorganisms that cause serious human and/or animal diseases, there is usually effective prophylaxis against them and the diseases caused are treatable [68].

Risk Group 1 of biological agents

The largest number of 99 (51.0%) sp. and ssp. is present in the Risk Group 1 of biological agents (that are unlikely to cause human disease); they are rarely associated with disease. In clinical laboratories, these mycobacterial species are isolated from clinical samples (sputum, tissue, urine, etc.) without clinical relevance [69]. A similar situation was documented in the Czech Republic [41]. In our study, we isolated 14 sp. and ssp. from this Risk Group 1 (Table 1).

The spectrum of NTM spp. varies widely depending upon the source of the environmental samples in different locations in the Czech Republic and the material sampled: bat guano, earthworm faeces, woody material, soil, etc. Of the 14 NTM sp. mentioned above in this study, only 6 sp. were demonstrated in other localities: M. duvalii, M. gordonae, M. hassiacum, M. kumamotonense, M. terrae, and M. triviale [41]. The remaining 8 sp. (M. arcueilense, M. chlorophenolicum, M. chubuense, M. gilvum, M. insubricum, M. montmartrense, M. psychrotolerans, and M. rhodesiae) were found only in this karstic watershed (Table 1). In the previous study in the Moravian Karst, four of these NTM were already proven; in the Bull Rock Cave, M. chubuense, M. gilvum, M. insubricum, and M. rhodesiae were detected [47]. The last four NTM (M. arcueilense, M. chlorophenolicum, M. montmartrense, and M. psychrotolerans) were isolated in this watershed for the first time (Table 1) [47, 70].

In Hranice Karst (Czech Republic; CR), 80 km from the Moravian Karst, 8 NTM sp. and one complex were cultured. M. arupense, M. avium, M. florentinum, M. gordonae, M. intracellulare, M. mucogenicum, M. sediminis, and M. avium complex were isolated from sediments in Hranice Abyss and Zbrašov Aragonite Caves [68]. Except of M. florentinum and M. sediminis all other 6 NTM sp. were detected in the current study in Moravian Karst (Table 1); these two species remain unique in Hranice Karst [48].

These results suggest that different environmental niches create favourable conditions for different NTMs [65], which most likely specialize and colonize the substrates available therein. The exact geochemical parameters and conditions for colonizing of these substrates by environmental NTM are not yet known and explained in the published literature. Therefore, it is necessary to consider that people who live in this environment are exposed to these environmental NTM without any clinical symptoms. Many of these environmental NTMs are considered clinically irrelevant [4, 71].

Risk Group 2 of biological agents

We detected 14 sp., ssp., and complexes included in the Risk Group 2 of biological agents (Table 1), which can cause human disease (it is unlikely to spread to the community, and there is usually effective prophylaxis or treatment available) [71]. In this Risk Group 2 there are 87 (44.9%) out of 195 validated sp. and ssp.

In the Czech Republic, between the years 2003–2018, a total of 79%. mycobacterioses were caused in children by M. avium (included in these statistics were M. avium and M. intracellulare) [72]. In adult patients with mycobacteriosis, members of the M. avium-intracellulare complex were among the most common causative agents of infection [73]. Our preliminary data shows these infections were caused especially by M. avium ssp. hominissuis (unpublished data). Not surprisingly, in this study, M. avium ssp. hominissuis was cultured from water sediments from all three different types of samples collected in our study (Table 1). Due to this fact, water sediments could represent an infection risk for susceptible children and adults.

Risk Group 3 of biological agents

We did not detect any of the members of Risk Group 3 mycobacteria they are obligate pathogens and only transiently isolated from the environment (Table 1).

Impact of villages on water pollution in area of our interest (Moravian Karst)

In a just published study about Moravian Karst, our area of interest, the impact of villages on pollution by allogeneic was analysed and confirmed [27]. We have found a similar effect of settlements in the same villages and watersheds of streams (Tables 2–4; Figs. 1A–B, 2).

Higher phosphorus concentration in wastewater connected with villages could be beneficial for NTM growth as is in accordance with the results of the study published previously [74] where Mycobacterium spp. genes in reclaimed systems positively correlated with phosphorus. These findings suggest that phosphorous could be a growth- or survival-limiting nutrient for NTM. The phosphorus concentration was only one parameter statistically significantly increased in samples originating from sampling sites collected within the villages, downstream of the WWTPs outflows (SS-C). It is important to note that in our study, the spectrum of elements analysed in water was lower than the spectrum of elements in sediments (Tables 2, 3, and 4). However, nitrogen concentrations and its forms (ammonia and nitrates) did not correlate in water and water sediments (Tables 3 and 4).

The predominance of NTM in drinking water distribution systems [75] also depends on “water age” (esp. long-time standing water in pipes or water reservoirs) and sufficient residual monochloramine in various sections of the potable water systems [59].

Only 6 (21.4%) of 28 detected NTM complex and sp. in sediments in our study (MAC, M. gordonae, M. arupense, M. fortuitum, M. peregrinum, and M. septicum) matched the 12 NTM sp. detected in ponds and water reservoirs sediments in the Czech Republic [51]. The correlations between the occurrence of NTM and environmental, climatic, water, and water sediment characteristics have been described [76, 77]. The critical factor increasing the occurrence of NTM in water and aerobic water sediments was acidification. This parameter did not affect NTM positivity in water sediments in our study because of a very narrow range of all pH values (5.83–6.96) among the studied types of sampling locations. However, pH values of sediments in both sites in investigated areas (SS-B and SS-C) were statistically significantly higher compared to upstream of the sampling sites, where there were no differences in NTM positivity. While nutrients and organic carbon concentrations have been frequently reported to influence microbial communities, we did not confirm higher nitrogen or carbon concentration in villages’ area sediments (SS-B and SS-C) as NTM nutrient factors.

Tourists often consider brooks, rivers, and adjacent areas as attractive recreational places. According to our findings, these water streams could represent a similar risk to urban recreational water [78]. The risk of infection is also posed by situations after extreme events (e.g., Hurricanes Harvey and Irma in 2017 in the USA), during which local flooding occurs. During them, various pathogenic bacteria, including representatives of the Mycobacterium genus belonging to the Risk Groups 1 and 2, are washed away and spread in the environment [79].

A higher population per square mile, proportion of area as surface water, evapotranspiration, and copper and sodium soil levels were described that significantly increase the risk for pulmonary disease caused by NTM in the USA [80]. Our study showed the presence of PPM (M. avium spp. hominissuis, MAC, M. chelonae, M. fortuitum, M. intracellulare, and M. monacense) in all types studied sampling sites, although their diversity was higher in sediments near villages (SS-B and SS-C). Water temperature, pH, phosphorus, copper, and zinc concentrations in sediment played a significant role in NTM sp. diversity (Tables 1–4). M. fortuitum and other NTM often be detected in wastewater and surface water in urbanized and suburbanized environments [56, 79, 91 − 84, 92 − 85].

ANOVA, Analysis of variance; ANC4.5, Acid Neutralization Capacity to pH 4.5; CR, Czech Republic; DOC, Dissolve Oxygen Concentration; EC, Electrical Conductivity; ESM, Environmentally Saprophytic Mycobacteria; EU, European Union; HSD, Honestly Significant Difference; ICP-MS, Inductively Coupled Plasma Mass Spectrometry; LOD, Limit of Detection; LPSN, List of Prokaryotic Names with Standing in Nomenclature; V, Village; M., Mycobacterium; MAC, M. avium complex; MDR, Multi Drug Resistance; NTM, Nontuberculous mycobacteria; OPM, Obligatory Pathogenic Mycobacteria; ORP, Oxidation-Reduction Potential; PERMANOVA, Pairwise Permutational Multivariate Analysis Of Variance; PLA, Protected Landscape Area; PPM, Potentially Pathogenic Mycobacteria; sp., species; ssp., subspecies; SS-A, Sampling Site A; TC, Total Carbon; TIC, Total Inorganic Carbon; TOC, Total Organic Carbon; TRBA, Technical Rules for Biological Agents; WGS, Whole Genome Sequencing; WWTPs, Wastewater Treatment Plants.

Acknowledgements Authors thank Milan Bartos, Petra Hribova, and Michael Kobza from Masaryk University Brno, Czech Republic for technical support, consultation, and cooperation during sampling.

Author contributions H. Modra: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Writing - original draft, Visualization. V. Ulmann: Methodology, Validation, Formal analysis, Investigation, Writing - original draft. M. Gersl: Methodology, Validation, Formal analysis, Investigation, Data curation. V. Babak: Methodology, Validation, Biostatistical Analyses. O. Konecny: Conceptualization, Visualization, Methodology, Data curation. D. Hubelova: Methodology, Data Collection, Data Curation, Demographical Analyses. J. Caha: Methodology, Validation, Biostatistical Analyses. J. Kudelka: Methodology, Data Collection, Data Curation. J. O. Falkinham III: Conceptualization, Writing - review & editing. I. Pavlik: Writing – review & editing, Supervision, Funding acquisition.

Funding This work was funded by Czech Health Research Council (No. NU20-09-00114).

Data Availability The datasets generated and/or analysed during the current study is available in the Mendel University (FRDIS) repository and is attached in PDF file.

Compliance with Ethical Standards

Conflicts of Interest All authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Pavlik I, Falkinham J (2009) The occurrence of pathogenic and potentially pathogenic mycobacteria in animals and the role of the environment in the spread of infection, Chapter 6. In The Ecology of Mycobacteria: Impact on Animal’s and Human’s Health, 1^st ed.; Kazda J, Pavlik I, Falkinham IIIJO, Hruska K (Eds.) Springer: Dordrecht, The Netherlands; Heidelberg, Germany; London, UK; New York, NY, USA, 2009; pp. 199–281.
Falkinham JOIII (2021) Ecology of nontuberculous mycobacteria. Microorganisms 9(11):2262.
Walsh CM, Gebert MJ, Delgado-Baquerizo M, Maestre FT, Fierer N (2019) A global survey of mycobacterial diversity in soil. Appl Environ Microbiol 85(17):e01180-19.
Pavlik I, Ulmann V, Hubelova D, Weston RT (2022) Nontuberculous mycobacteria as sapronoses: A review. Microorganisms 10:1345.
Hubelova D, Ulmann V, Mikuska P, Licbinsky R, Alexa L, Modra H, Gersl M, Babak V, Weston RT, Pavlik I (2021) Nontuberculous mycobacteria prevalence in aerosol and spiders’ webs in karst caves: Low risk for speleotherapy. Microorganisms 9:2573.
Dong P, Cui Q, Fang T, Huang Y, Wang H (2019) Occurrence of antibiotic resistance genes and bacterial pathogens in water and sediment in urban recreational water. J Environ Sci (China) 77:65–74.
Virdi R, Lowe ME, Norton GJ, Dawrs SN, Hasan NA, Epperson LE, Glickman CM, Chan ED, Strong M, Crooks JL, Honda JR (2021) Lower recovery of nontuberculous mycobacteria from outdoor Hawai'i environmental water biofilms compared to indoor samples. Microorganisms 9(2):224.
Roguet A, Therial C, Saad M, Boudahmane L, Moulin L, Lucas FS (2016) High mycobacterial diversity in recreational lakes. Antonie van Leeuwenhoek 109:619–631.
Chen X, Wang Y, Li W, Zhao X, Lu Y, Yu Y, Chen S, Ding Z (2020) Microbial contamination in distributed drinking water purifiers induced by water stagnation. Environ Res 188:109715.
Moreno-Mesonero L, Ferrus MA, Moreno Y (2020) Determination of the bacterial microbiome of free-living amoebae isolated from wastewater by 16S rRNA amplicon-based sequencing. Environ Res 190:109987.
Falkinham JOIII (2009) Surrounded by mycobacteria: Nontuberculous mycobacteria in the human environment. J Appl Microbiol 107:356–367.
Falkinham JOIII (2016) Current epidemiologic trends of the nontuberculous mycobacteria (NTM). Curr Environ Health Rep 3:161–167.
Roguet A, Therial C, Catherine A, Bressy A, Varrault G, Bouhdamane L, Tran V, Lemaire BJ, Vincon-Leite B, Saad M, Moulin L, Lucas FS (2018) Importance of local and regional scales in shaping mycobacterial abundance in freshwater lakes. Microb Ecol 75(4):834–846.
Vega L, Jaimes J, Morales D, Martínez D, Cruz-Saavedra L, Muñoz M, Ramírez JD (2021) Microbial communities' characterization in urban recreational surface waters using Next Generation Sequencing. Microb Ecol 81(4):847–863.
Brennan PJ, Nikaido H (1995) The envelope of mycobacteria. Annu Rev Biochem 64:29–63.
Jesenska A, Damborsky J (2009) Degradation of organic compounds by mycobacteria. In The Ecology of Mycobacteria: Impact on Animal’s and Human’s Health, 1^st ed.; Kazda J, Pavlik I, Falkinham IIIJO, Hruska K (Eds.) Springer: Dordrecht, The Netherlands; Heidelberg, Germany; London, UK; New York, NY, USA, 2009; pp. 314–326.
Pontiroli A, Khera TT, Oakley BB, Mason S, Dowd SE, Travis ER, Erenso G, Aseffa A, Courtenay O, Wellington EMH (2013) Prospecting environmental mycobacteria: Combined molecular approaches reveal unprecedented diversity. PLOS One 8:e68648.
Stout JE, Koh WJ, Yew WW (2016) Update on pulmonary disease due to non-tuberculous mycobacteria. Int J Infect Dis 45:123–134.
Totaro M, Casini B, Valentini P, Miccoli M, Lopalco PL, Baggiani A (2018) Assessing natural mineral water microbiology quality in the absence of cultivable pathogen bacteria. J Water Health 16:425–434.
Varghese B, Al-Hajoj S (2020) A global update on rare non-tuberculous mycobacteria in humans: Epidemiology and emergence. Int J Tuberc Lung Dis 24:214–223.
Rusterholtz KJ, Mallory LM (1994) Density, activity, and diversity of bacteria indigenous to a karstic aquifer. Microb Ecol 28(1):79–99.
Heinz B, Birk S, Liedl R, Geyer T, Straub KL, Andersen J, Bester K, Kappler A (2009) Water quality deterioration at karst spring (Gallusquelle, Germany) due to combined sewer overflow: Evidence of bacterial and micro-pollutant contamination. Environ Geol 57:797–808.
Chiffre A, Degiorgi F, Bulete A, Spinner L, Badot PM (2016) Occurrence of pharmaceuticals in WWTP effluents and their impact in a karstic rural catchment of Eastern France. Environ Sci Pollut Res 23:25427–25441.
Gutierrez M, Wu SS, Rodriguez JR, Jones AD, Lockwood BE (2016) Assessing the state of contamination in a historic mining town using sediment chemistry. Arch Environ Contam Toxicol 70:747–756.
Tagne GV, Dowling C (2020) Land-use controls on nutrient loads in aquifers draining agricultural and mixed-use karstic watersheds. Environ Monit Assess 192(3):168.
Sun J, Takahashi Y, Strosnider WHJ, Kogure T, Wang B, Wu P, Zhu L, Dong Z (2021) Identification and quantification of contributions to karst groundwater using a triple stable isotope labelling and mass balance model. Chemosphere 263:127946.
Mala J, Hrich K, Vaculikova K, Lejska S (2022) Multicriterial approach to the determination of buffer zones for the Moravian Karst protected landscape area in the Czech Republic. Environ Monit Assess 194(2):103.
Pronk M, Goldscheider N, Zopfi J (2006) Dynamics and interaction of organic carbon, turbidity and bacteria in karst aquifer system. Hydrogeol J 14: 473–484.
Zhang Y, Kelly WR, Panno SV, Liu WT (2014) Tracing fecal pollution sources in karst groundwater by Bacteroidales genetic biomarkers, bacterial indicators, and environmental variables. Sci Total Environ 490:1082–1090.
Diston D, Sinreich M, Zimmermann S, Baumgartner A, Felleisen R (2015) Evaluation of molecular- and culture-dependent MST markers to detect fecal contamination and indicate viral presence in good quality groundwater. Environ Sci Technol 49:7142–7151.
He Q, Qui S, Jiang Y, Wu Z, Liu Z (2016) Land-use change caused microbial pollution in a karst underground river, Chongqing, China. Environ Earth Sci 75:709.
Diston D, Robbi R, Baumgartner A, Felleisen R (2018) Microbial source tracking in highly vulnerable karst drinking water resources. J Water Health 16:138–149.
Bandy AM, Cook K, Fryar AE, Zhu J (2020) Differential Transport of Escherichia coli isolates compared to abiotic tracers in a karst aquifer. Ground Water 58:70–78.
Buckerfield SJ, Quilliam RS, Waldron S, Naylor LA, Li S, Oliver DM (2019) Rainfall-driven E. coli transfer to the stream-conduit network observed through increasing spatial scales in mixed land-use paddy farming karst terrain. Water Res X 5:100038.
Buckerfield SJ, Waldron S, Quilliam RS, Naylor LA, Li S, Oliver DM (2019) How can we improve understanding of faecal indicator dynamics in karst systems under changing climatic, population, and land use stressors? - Research opportunities in SW China. Sci Total Environ 646:438–447.
Buckerfield SJ, Quilliam RS, Bussiere L, Waldron S, Naylor LA, Li S, Oliver DM (2020) Chronic urban hotspots and agricultural drainage drive microbial pollution of karst water resources in rural developing regions. Sci Total Environ 744:140898.
Hromas J (2009) Caves. In: Mackovcin P, Sedlacek M (eds): Protected Areas in the Czech Republic. Agentura Ochrany Prirody a Krajiny CR a EkoCentrum Brno, Praha. 608 pp. (in Czech).
Kolejka J, Mackovcin P, Palensky P (2009) Natural Landscape, Chapter 4. In Landscape Atlas of the Czech Republic, 1^st ed.; Hrnciarova T, Mackovcin P, Zvara I et al., Ministry of the Environment of the Czech Republic, Prague, The Silva Tarouca Research Institute for Landscape and Ornamental Gardening, p. r. i., Pruhonice, pp. 98–156. (in Czech).
Czech Statistical Office (2017) Retrieved from Czech Statistical Office | CZSO
Ulmann V, Kracalikova A, Dziedzinska R (2015) Mycobacteria in water used for personal hygiene in heavy industry and collieries: A potential risk for employees. Int J Environ Res Public Health 12:2870–2877.
Ulmann V, Modra H, Babak V, Weston RT, Pavlik I (2021) Recovery of mycobacteria from heavily contaminated environmental matrices. Microorganisms 9:2178.
Portaels F, De Muynck A, Sylla MP (1988) Selective isolation of mycobacteria from soil: A statistical analysis approach. J Gen Microbiol 134:849–855.
Ulmann V, Modra H, Babak V, Weston RT, Pavlik I (2021) Recovery of mycobacteria from heavily contaminated environmental matrices. Microorganisms. 9(10):2178.
Engbaek HC, Vergmann B, Baess I, Will DW (1967) M. xenopei: A bacteriological study of M. xenopei including case reports of Danish patients. Acta Pathol Microbiol Scand 69:577–594.
Brooks RW, George KL, Parker BC, Falkinham JOIII, Gruff H (1984) Recovery and survival of nontuberculous mycobacteria under various growth and decontamination conditions. Can J Microbiol 30:1112–1117.
Shannon CE, Weaner W (1949) The mathematical theory of communication. Urbana, Illinois. University of Illinois Press. 177 pp.
Modra H, Bartos M, Hribova P, Ulmann V, Hubelova D, Konecny O, Gersl M, Kudelka J, Voros D, Pavlik I (2017) Detection of mycobacteria in the environment of the Moravian Karst (Bull Rock Cave and the relevant water catchment area): The impact of water sediment, earthworm castings and bat guano. Vet Med-Czech 62:153–168.
Pavlik I, Gersl M, Bartos M, Ulmann V, Kaucka P, Caha J, Unc A, Hubelova D, Konecny O, Modra H (2018) Nontuberculous mycobacteria in the environment of Hranice Abyss, the world's deepest flooded cave (Hranice Karst, Czech Republic). Environ Sci Pollut Res Int 25:23712–23724.
Pavlik I, Falkinham J, Kazda J (2009) Environments providing favourable conditions for the multiplication and transmission of mycobacteria, Chapter 5. In The Ecology of Mycobacteria: Impact on Animal’s and Human’s Health, 1^st ed.; Kazda J, Pavlik I, Falkinham IIIJO, Hruska K (Eds.) Springer: Dordrecht, The Netherlands; Heidelberg, Germany; London, UK; New York, NY, USA, 2009; pp. 89–197.
Chandran A, Varghese S, Kandeler E, Thomas A, Hatha M, Mazumder A (2011) An assessment of potential public health risk associated with the extended survival of indicator and pathogenic bacteria in freshwater lake sediments. Int J Hyg Environ Health 214:258–264.
Makovcova J, Slany M, Babak V, Slana I, Kralik P (2014) The water environment as a source of potentially pathogenic mycobacteria. J Water Health 12:254–263.
Ashbolt NJ (2015) Environmental (saprozoic) pathogens of engineered water systems: Understanding their ecology for risk assessment and management. Pathogens 4:390–405.
Webster TM, McFarland A, Gebert MJ, Oliverio AM, Nichols LM, Dunn RR, Hartmann EM, Fierer N (2021) Structure and functional attributes of bacterial communities in premise plumbing across the United States. Environ Sci Technol 55(20):14105–14114.
Isaac TS, Sherchan SP (2020) Molecular detection of opportunistic premise plumbing pathogens in rural Louisiana's drinking water distribution system. Environ Res 181:108847.
Learbuch KLG, Smidt H, van der Wielen PWJJ (2022) Water and biofilm in drinking water distribution systems in the Netherlands. Sci Total Environ 831:154940.
Zhang H, Ma M, Huang T, Miao Y, Li H, Liu K, Yang W, Ma B (2022) Spatial and temporal dynamics of Actinobacteria in drinking water reservoirs: Novel insights into abundance, community structure, and co-existence model. Sci Total Environ 814:152804.
Taylor RH, Falkinham JOIII, Norton CD, LeChevallier MW (2000) Chlorine-, chloramine-, chlorine dioxide- and ozone-susceptibility of Mycobacterium avium. Appl Environ Microbiol 66:1702–1705.
Gomez-Alvarez V, Revetta RP, Santo Domingo JW (2012) Metagenomic analyses of drinking water receiving different disinfection treatments. Appl Environ Microbiol 78:6095–6102.
Kitajima M, Cruz MC, Williams RBH, Wuertz S, Whittle AJ (2021) Microbial abundance and community composition in biofilms on in-pipe sensors in a drinking water distribution system. Sci Total Environ 766:142314.
Vaerewijck MJM, Huys G, Palomino JC, Swings J, Portaels F (2005) Mycobacteria in drinking water distribution systems: Ecology and significance for human health. FEMS Microbiol Rev 29:911–934.
Schwabacher H (1959) A strain of Mycobacterium isolated from skin lesions of a cold-blooded animal, Xenopus laevis, and its relation to atypical acid-fast bacilli occurring in man. J Hyg (Lond) 57:57–67.
Pavlik I, Ulmann V, Modra H, Gersl M, Rantova B, Zukal J, Zukalova K, Konecny O, Kana V, Kubalek P, Babak V, Weston RT (2021) Nontuberculous mycobacteria prevalence in bats’ guano from caves and attics of buildings studied by culture and qPCR examinations. Microorganisms 9:2236.
Cui Q, Huang Y, Wang H, Fang T (2019) Diversity and abundance of bacterial pathogens in urban rivers impacted by domestic sewage. Environ Pollut 249:24–35.
Atashgahi S, Aydin R, Dimitrov MR, Sipkema D, Hamons K, Lahti L, Maphosa F, Kruse T, Saccenti E, Springael D, Dejonghe W, Smidt H (2015) Impact of wastewater treatment plant on microbial community composition and function in a hyporheic zone of a eutrophic river. Sci Rep 5:17284.
Kazda J (2000) The Ecology of Mycobacteria; Kluwer Academic Publishers: Dordrecht, Germany; Boston, MA, USA; London, UK, 2000; 72p.
Ye L, Zhang T (2013) Bacterial communities in different sections of a municipal wastewater treatment plant revealed by 16S rRNA 454 pyrosequencing. Appl Microbiol Biotechnol 97:2681–2690.
Amha YM, Anwar MZ, Kumaraswamy R, Henschel A, Ahmad F (2017) Mycobacteria in municipal wastewater treatment and reuse: Microbial diversity for screening the occurrence of clinically and environmentally relevant species in arid regions. Environ Sci Technol 51:3048–3056.
Directive 2000/54/EC (2000) European Parliament and of the Council of 18 September 2000 on the protection of workers from risks related to exposure to biological agents at work. Off J Eur Communities L262:21–45.
Alffenaar JW, Märtson AG, Heysell SK, Cho JG, Patanwala A, Burch G, Kim HY, Sturkenboom MGG, Byrne A, Marriott D, Sandaradura I, Tiberi S, Sintchencko V, Srivastava S, Peloquin CA (2021) Therapeutic drug monitoring in non-tuberculosis mycobacteria infections. Clin Pharmacokinet 60(6):711–725.
Modra H, Gruberova E, Konecny O, Ulmann V, Kaucka P, Vlkova M, Tuma A, Halesova T, Kudelka J, Gersl M, Pavlik I (2018) Influx and concentration of triazine pesticides in the Amaterska Cave System, Moravian Karst, Czech Republic. J Soils Sediments 18:640–647.
LPSN: List of Prokaryotic Names with Standing in Nomenclature. https://lpsn.dsmz.de/. (Accessed 1^st July 2023).
Dolezalova K, Maly M, Wallenfels J, Gopfertova D (2021) Nontuberculous mycobacterial infections in children in the Czech Republic in the period 2003–2018. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub 165(3):277–282.
Modra H, Ulmann V, Caha J, Hubelova D, Konecny O, Svobodova J, Weston RT, Pavlik I (2019) Socio-economic and environmental factors related to spatial differences in human non-tuberculous mycobacterial diseases in the Czech Republic. Int J Environ Res Public Health 16:3969.
Garner E, McLain J, Bowers J, Engelthaler DM, Edwards MA, Pruden A (2018) Microbial ecology and water chemistry impact regrowth of opportunistic pathogens in full-scale reclaimed water distribution systems. Environ Sci Technol 52:9056−9068.
Dowdell K, Haig SJ, Caverly LJ, Shen Y, LiPuma JJ, Raskin L (2019) Nontuberculous mycobacteria in drinking water systems - the challenges of characterization and risk mitigation. Curr Opin Biotechnol 57:127–136.
Iivanainen EK, Martikainen PJ, Vaananen PK, Katila ML (1993) Environmental factors affecting the occurrence of mycobacteria in brook waters. Appl Environ Microbiol 59:398–404.
Iivanainen E, Martikainen PJ, Vaananen P, Katila ML (1999) Environmental factors affecting the occurrence of mycobacteria brook sediments. J Appl Microbiol 86:673–681.
Fang T, Cui Q, Huang Y, Dong P, Wang H, Liu WT, Ye Q (2018) Distribution comparison and risk assessment of free-floating and particle-attached bacterial pathogens in urban recreational water: Implications for water quality management. Sci Total Environ 613–614:428–438.
Mapili K, Rhoads WJ, Coughter M, Pieper KJ, Edwards MA, Pruden A (2022) Occurrence of opportunistic pathogens in private wells after major flooding events: A four state molecular survey. Sci Total Environ 20, 826:153901.
Adjemian J, Olivier KN, Seitz AE, Falkinham III JO, Holland SM, Prevots R (2012) Spatial clusters of nontuberculous mycobacterial lung disease in the United States. Am J Respir Crit Care Med 186:553–558.
Pavlik I, Ulmann V, Weston RT (2021) Clinical relevance and environmental prevalence of Mycobacterium fortuitum group members: Comment on Mugetti et al. Gene sequencing and phylogenetic analysis: Powerful tools for an improved diagnosis of fish mycobacteriosis caused by Mycobacterium fortuitum group members. Microorganisms 9, 797. Microorganism 9:2345.
Gregson BH, Bani A, Steinfield L, Holt D, Whitby C (2022) Anaerobes and methanogens dominate the microbial communities in water harvesting ponds used by Kenyan rural smallholder farmers. Sci Total Environ 819:153040.

No competing interests reported.

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The impact of residential villages in a karstic watershed on numbers and diversity of nontuberculous mycobacteria

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